Publications
Here you can find our latest publications. We are working on deep learning-based microscopy applications, such as pathology:
2024
Qiu, Jingna; Aubreville, Marc; Wilm, Frauke; Öttl, Mathias; Utz, Jonas; Schlereth, Maja; Breininger, Katharina
Leveraging image captions for selective whole slide image annotation Proceedings Article
In: Proceedings of MICCAI, 2024.
Abstract | Links | BibTeX | Tags:
@inproceedings{qiu_leveraging_2024,
title = {Leveraging image captions for selective whole slide image annotation},
author = {Jingna Qiu and Marc Aubreville and Frauke Wilm and Mathias Öttl and Jonas Utz and Maja Schlereth and Katharina Breininger},
url = {https://arxiv.org/abs/2407.06363},
doi = {10.48550/ARXIV.2407.06363},
year = {2024},
date = {2024-01-01},
urldate = {2024-09-26},
booktitle = {Proceedings of MICCAI},
abstract = {Acquiring annotations for whole slide images (WSIs)-based deep learning tasks, such as creating tissue segmentation masks or detecting mitotic figures, is a laborious process due to the extensive image size and the significant manual work involved in the annotation. This paper focuses on identifying and annotating specific image regions that optimize model training, given a limited annotation budget. While random sampling helps capture data variance by collecting annotation regions throughout the WSIs, insufficient data curation may result in an inadequate representation of minority classes. Recent studies proposed diversity sampling to select a set of regions that maximally represent unique characteristics of the WSIs. This is done by pretraining on unlabeled data through self-supervised learning and then clustering all regions in the latent space. However, establishing the optimal number of clusters can be difficult and not all clusters are task-relevant. This paper presents prototype sampling, a new method for annotation region selection. It discovers regions exhibiting typical characteristics of each task-specific class. The process entails recognizing class prototypes from extensive histopathology image-caption databases and detecting unlabeled image regions that resemble these prototypes. Our results show that prototype sampling is more effective than random and diversity sampling in identifying annotation regions with valuable training information, resulting in improved model performance in semantic segmentation and mitotic figure detection tasks. Code is available at https://github.com/DeepMicroscopy/Prototype-sampling.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Qiu, Jingna; Aubreville, Marc; Wilm, Frauke; Öttl, Mathias; Utz, Jonas; Schlereth, Maja; Breininger, Katharina
Leveraging Image Captions for Selective Whole Slide Image Annotation Book Section
In: Linguraru, Marius George; Dou, Qi; Feragen, Aasa; Giannarou, Stamatia; Glocker, Ben; Lekadir, Karim; Schnabel, Julia A. (Ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, vol. 15012, pp. 207–217, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-72389-6 978-3-031-72390-2.
@incollection{linguraru_leveraging_2024,
title = {Leveraging Image Captions for Selective Whole Slide Image Annotation},
author = {Jingna Qiu and Marc Aubreville and Frauke Wilm and Mathias Öttl and Jonas Utz and Maja Schlereth and Katharina Breininger},
editor = {Marius George Linguraru and Qi Dou and Aasa Feragen and Stamatia Giannarou and Ben Glocker and Karim Lekadir and Julia A. Schnabel},
url = {https://link.springer.com/10.1007/978-3-031-72390-2_20},
doi = {10.1007/978-3-031-72390-2_20},
isbn = {978-3-031-72389-6 978-3-031-72390-2},
year = {2024},
date = {2024-01-01},
urldate = {2024-10-22},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2024},
volume = {15012},
pages = {207–217},
publisher = {Springer Nature Switzerland},
address = {Cham},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Pan, Zhaoya; Sievert, Matti; Ammeling, Jonas; Ganz, Jonathan; Oetter, Nicolai; Stelzle, Florian; Frenken, Ann-Kathrin; Breininger, Katharina; Goncalves, Miguel
Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2024, pp. 143–148, Springer Fachmedien Wiesbaden, Wiesbaden, 2024, ISBN: 978-3-658-44036-7 978-3-658-44037-4.
@incollection{maier_few_2024-1,
title = {Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors},
author = {Marc Aubreville and Zhaoya Pan and Matti Sievert and Jonas Ammeling and Jonathan Ganz and Nicolai Oetter and Florian Stelzle and Ann-Kathrin Frenken and Katharina Breininger and Miguel Goncalves},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-44037-4_42},
doi = {10.1007/978-3-658-44037-4_42},
isbn = {978-3-658-44036-7 978-3-658-44037-4},
year = {2024},
date = {2024-01-01},
urldate = {2024-02-21},
booktitle = {Bildverarbeitung für die Medizin 2024},
pages = {143–148},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2023
Haghofer, Andreas; Fuchs-Baumgartinger, Andrea; Lipnik, Karoline; Klopfleisch, Robert; Aubreville, Marc; Scharinger, Josef; Weissenböck, Herbert; Winkler, Stephan M.; Bertram, Christof A.
Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing Journal Article
In: Scientific Reports, vol. 13, no. 1, pp. 19436, 2023, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{haghofer_histological_2023,
title = {Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing},
author = {Andreas Haghofer and Andrea Fuchs-Baumgartinger and Karoline Lipnik and Robert Klopfleisch and Marc Aubreville and Josef Scharinger and Herbert Weissenböck and Stephan M. Winkler and Christof A. Bertram},
url = {https://www.nature.com/articles/s41598-023-46607-w},
doi = {10.1038/s41598-023-46607-w},
issn = {2045-2322},
year = {2023},
date = {2023-11-01},
urldate = {2024-11-04},
journal = {Scientific Reports},
volume = {13},
number = {1},
pages = {19436},
abstract = {Abstract
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification.
Utz, Jonas; Weise, Tobias; Schlereth, Maja; Wagner, Fabian; Thies, Mareike; Gu, Mingxuan; Uderhardt, Stefan; Breininger, Katharina
Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN Proceedings Article
In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 3858–3866, IEEE, Paris, France, 2023, ISBN: 9798350307443.
@inproceedings{utz_focus_2023,
title = {Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN},
author = {Jonas Utz and Tobias Weise and Maja Schlereth and Fabian Wagner and Mareike Thies and Mingxuan Gu and Stefan Uderhardt and Katharina Breininger},
url = {https://ieeexplore.ieee.org/document/10350420/},
doi = {10.1109/ICCVW60793.2023.00417},
isbn = {9798350307443},
year = {2023},
date = {2023-10-01},
urldate = {2024-02-21},
booktitle = {2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
pages = {3858–3866},
publisher = {IEEE},
address = {Paris, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ammeling, Jonas; Manger, Carina; Kwaka, Elias; Krügel, Sebastian; Uhl, Matthias; Kießig, Angelika; Fritz, Alexis; Ganz, Jonathan; Riener, Andreas; Bertram, Christof A.; Breininger, Katharina; Aubreville, Marc
Appealing but Potentially Biasing – Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making Proceedings Article
In: MuC ’23: Mensch und Computer 2023, pp. 330–335, ACM, Rapperswil Switzerland, 2023, ISBN: 9798400707711.
@inproceedings{ammeling_appealing_2023,
title = {Appealing but Potentially Biasing - Investigation of the Visual Representation of Segmentation Predictions by AI Recommender Systems for Medical Decision Making},
author = {Jonas Ammeling and Carina Manger and Elias Kwaka and Sebastian Krügel and Matthias Uhl and Angelika Kießig and Alexis Fritz and Jonathan Ganz and Andreas Riener and Christof A. Bertram and Katharina Breininger and Marc Aubreville},
url = {https://dl.acm.org/doi/10.1145/3603555.3608561},
doi = {10.1145/3603555.3608561},
isbn = {9798400707711},
year = {2023},
date = {2023-09-01},
urldate = {2023-09-04},
booktitle = {MuC '23: Mensch und Computer 2023},
pages = {330–335},
publisher = {ACM},
address = {Rapperswil Switzerland},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Fragoso-Garcia, Marco; Wilm, Frauke; Bertram, Christof A.; Merz, Sophie; Schmidt, Anja; Donovan, Taryn; Fuchs-Baumgartinger, Andrea; Bartel, Alexander; Marzahl, Christian; Diehl, Laura; Puget, Chloe; Maier, Andreas; Aubreville, Marc; Breininger, Katharina; Klopfleisch, Robert
Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images Journal Article
In: Veterinary Pathology, pp. 03009858231189205, 2023, ISSN: 0300-9858, 1544-2217.
Abstract | Links | BibTeX | Tags:
@article{fragoso-garcia_automated_2023,
title = {Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images},
author = {Marco Fragoso-Garcia and Frauke Wilm and Christof A. Bertram and Sophie Merz and Anja Schmidt and Taryn Donovan and Andrea Fuchs-Baumgartinger and Alexander Bartel and Christian Marzahl and Laura Diehl and Chloe Puget and Andreas Maier and Marc Aubreville and Katharina Breininger and Robert Klopfleisch},
url = {http://journals.sagepub.com/doi/10.1177/03009858231189205},
doi = {10.1177/03009858231189205},
issn = {0300-9858, 1544-2217},
year = {2023},
date = {2023-07-01},
urldate = {2023-08-07},
journal = {Veterinary Pathology},
pages = {03009858231189205},
abstract = {Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Hirling, Dominik; Tasnadi, Ervin; Caicedo, Juan; Caroprese, Maria V.; Sjögren, Rickard; Aubreville, Marc; Koos, Krisztian; Horvath, Peter
Segmentation metric misinterpretations in bioimage analysis Journal Article
In: Nature Methods, 2023, ISSN: 1548-7091, 1548-7105.
Abstract | Links | BibTeX | Tags:
@article{hirling_segmentation_2023,
title = {Segmentation metric misinterpretations in bioimage analysis},
author = {Dominik Hirling and Ervin Tasnadi and Juan Caicedo and Maria V. Caroprese and Rickard Sjögren and Marc Aubreville and Krisztian Koos and Peter Horvath},
url = {https://www.nature.com/articles/s41592-023-01942-8},
doi = {10.1038/s41592-023-01942-8},
issn = {1548-7091, 1548-7105},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-28},
journal = {Nature Methods},
abstract = {Abstract
Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Quantitative evaluation of image segmentation algorithms is crucial in the field of bioimage analysis. The most common assessment scores, however, are often misinterpreted and multiple definitions coexist with the same name. Here we present the ambiguities of evaluation metrics for segmentation algorithms and show how these misinterpretations can alter leaderboards of influential competitions. We also propose guidelines for how the currently existing problems could be tackled.
Aubreville, Marc; Wilm, Frauke; Stathonikos, Nikolas; Breininger, Katharina; Donovan, Taryn A.; Jabari, Samir; Veta, Mitko; Ganz, Jonathan; Ammeling, Jonas; Diest, Paul J. Van; Klopfleisch, Robert; Bertram, Christof A.
A comprehensive multi-domain dataset for mitotic figure detection Journal Article
In: Scientific Data, vol. 10, no. 1, pp. 484, 2023, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{aubreville_comprehensive_2023,
title = {A comprehensive multi-domain dataset for mitotic figure detection},
author = {Marc Aubreville and Frauke Wilm and Nikolas Stathonikos and Katharina Breininger and Taryn A. Donovan and Samir Jabari and Mitko Veta and Jonathan Ganz and Jonas Ammeling and Paul J. Van Diest and Robert Klopfleisch and Christof A. Bertram},
url = {https://www.nature.com/articles/s41597-023-02327-4},
doi = {10.1038/s41597-023-02327-4},
issn = {2052-4463},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-26},
journal = {Scientific Data},
volume = {10},
number = {1},
pages = {484},
abstract = {Abstract
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.
Ammeling, Jonas; Wilm, Frauke; Ganz, Jonathan; Breininger, Katharina; Aubreville, Marc
Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge Book Section
In: Sheng, Bin; Aubreville, Marc (Ed.): Mitosis Domain Generalization and Diabetic Retinopathy Analysis, vol. 13597, pp. 201–205, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-33657-7 978-3-031-33658-4, (Series Title: Lecture Notes in Computer Science).
@incollection{sheng_reference_2023,
title = {Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge},
author = {Jonas Ammeling and Frauke Wilm and Jonathan Ganz and Katharina Breininger and Marc Aubreville},
editor = {Bin Sheng and Marc Aubreville},
url = {https://link.springer.com/10.1007/978-3-031-33658-4_19},
doi = {10.1007/978-3-031-33658-4_19},
isbn = {978-3-031-33657-7 978-3-031-33658-4},
year = {2023},
date = {2023-05-01},
urldate = {2023-06-30},
booktitle = {Mitosis Domain Generalization and Diabetic Retinopathy Analysis},
volume = {13597},
pages = {201–205},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Sheng, Bin; Aubreville, Marc (Ed.)
Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-33657-7 978-3-031-33658-4.
@book{sheng_mitosis_2023,
title = {Mitosis Domain Generalization and Diabetic Retinopathy Analysis: MICCAI Challenges MIDOG 2022 and DRAC 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18–22, 2022, Proceedings},
editor = {Bin Sheng and Marc Aubreville},
url = {https://link.springer.com/10.1007/978-3-031-33658-4},
doi = {10.1007/978-3-031-33658-4},
isbn = {978-3-031-33657-7 978-3-031-33658-4},
year = {2023},
date = {2023-05-01},
urldate = {2023-07-01},
volume = {13597},
publisher = {Springer Nature Switzerland},
address = {Cham},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Rampas, Dominic; Pernias, Pablo; Aubreville, Marc
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces Miscellaneous
2023, (arXiv:2211.07292 [cs]).
Abstract | Links | BibTeX | Tags:
@misc{rampas_novel_2023,
title = {A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces},
author = {Dominic Rampas and Pablo Pernias and Marc Aubreville},
url = {http://arxiv.org/abs/2211.07292},
year = {2023},
date = {2023-05-01},
urldate = {2023-07-03},
abstract = {Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity. Contemporary techniques enable the generation of highly intricate visuals which rapidly approach near-photorealistic quality. Nevertheless, as progress is achieved, the complexity of these methodologies increases, consequently intensifying the comprehension barrier between individuals within the field and those external to it. In an endeavor to mitigate this disparity, we propose a streamlined approach for text-to-image generation, which encompasses both the training paradigm and the sampling process. Despite its remarkable simplicity, our method yields aesthetically pleasing images with few sampling iterations, allows for intriguing ways for conditioning the model, and imparts advantages absent in state-of-the-art techniques. To demonstrate the efficacy of this approach in achieving outcomes comparable to existing works, we have trained a one-billion parameter text-conditional model, which we refer to as "Paella". In the interest of fostering future exploration in this field, we have made our source code and models publicly accessible for the research community.},
note = {arXiv:2211.07292 [cs]},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Sievert, M.; Aubreville, M.; Eckstein, M.; Mantsopoulos, K.; Koch, M.; Gostian, A. O.; Mueller, S. K.; Iro, H.; Goncalves, M.
Cellular density and variability in laryngeal and pharyngeal squamous cell carcinoma using confocal laser endomicroscopy Journal Article
In: European Review for Medical and Pharmacological Sciences, vol. 27, no. 8, pp. 3622–3630, 2023, ISSN: 1128-3602, 2284-0729.
Abstract | Links | BibTeX | Tags:
@article{sievert_cellular_2023,
title = {Cellular density and variability in laryngeal and pharyngeal squamous cell carcinoma using confocal laser endomicroscopy},
author = {M. Sievert and M. Aubreville and M. Eckstein and K. Mantsopoulos and M. Koch and A. O. Gostian and S. K. Mueller and H. Iro and M. Goncalves},
url = {https://doi.org/10.26355/eurrev_202304_32146},
doi = {10.26355/eurrev_202304_32146},
issn = {1128-3602, 2284-0729},
year = {2023},
date = {2023-04-01},
urldate = {2024-11-04},
journal = {European Review for Medical and Pharmacological Sciences},
volume = {27},
number = {8},
pages = {3622–3630},
abstract = {OBJECTIVE: Confocal laser endomicroscopy (CLE) allows the visualization of epithelium in a thousand-fold magnification. This study analyzes the architectural differences at the cellular level of the mucosa and squamous cell carcinoma (SCC). PATIENTS AND METHODS: A total of 60 CLE sequences recorded in 5 patients with SCC undergoing laryngectomy between October 2020 and February 2021 were analyzed. The corresponding histologic sample derived from H&E staining was assigned to each sequence, capturing CLE images of the tumor and healthy mucosa. In addition, the cellular structure analysis was performed to diagnose SCC by measuring the total number of cells and cell size in 60 sequences in a fixed field of view (FOV) with 240 μm in diameter (45,239 μm2). RESULTS: Out of 3,600 images, 1,620 (45%) showed benign mucosa and 1,980 (55%) SCC. The automated analysis yielded a difference in cell size, with healthy epithelial cells being 171.9±82.0 μm2 smaller than SCC cells, which were 246.3±171.9 μm2 and showed greater variability in size (p=0.037). In addition, due to the probe’s fixed FOV, there was a difference in cell count with a total of 188.7±38.3 and 124.8±38.6 cells in images of normal epithelium and SCC (p<0.001), respectively. Regarding cell density as a criterion for the differentiation of benign/malign, using a cut-off value of 145.5 cells/FOV, we obtained sensitivity and specificity of 88.0% and 71.9%, respectively. CONCLUSIONS: SCC reveals marked differences at a cellular level compared to the healthy epithelium. Our results further support the importance of this feature for identifying SCC during CLE imaging.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Öttl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina
Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology Proceedings Article
In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1–5, IEEE, Cartagena, Colombia, 2023, ISBN: 978-1-6654-7358-3.
@inproceedings{wilm_mind_2023,
title = {Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology},
author = {Frauke Wilm and Marco Fragoso and Christof A. Bertram and Nikolas Stathonikos and Mathias Öttl and Jingna Qiu and Robert Klopfleisch and Andreas Maier and Marc Aubreville and Katharina Breininger},
url = {https://ieeexplore.ieee.org/document/10230458/},
doi = {10.1109/ISBI53787.2023.10230458},
isbn = {978-1-6654-7358-3},
year = {2023},
date = {2023-04-01},
urldate = {2025-11-14},
booktitle = {2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)},
pages = {1–5},
publisher = {IEEE},
address = {Cartagena, Colombia},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ammeling, Jonas; Schmidt, Lars-Henning; Ganz, Jonathan; Niedermair, Tanja; Brochhausen-Delius, Christoph; Schulz, Christian; Breininger, Katharina; Aubreville, Marc
Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 220–225, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_attention-based_2023,
title = {Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays},
author = {Jonas Ammeling and Lars-Henning Schmidt and Jonathan Ganz and Tanja Niedermair and Christoph Brochhausen-Delius and Christian Schulz and Katharina Breininger and Marc Aubreville},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_48},
doi = {10.1007/978-3-658-41657-7_48},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {220–225},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Ganz, Jonathan; Ammeling, Jonas; Donovan, Taryn A.; Fick, Rutger Hj.; Breininger, Katharina; Bertram, Christof A.
Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 189–195, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_deep_2023,
title = {Deep Learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-free Object Detector},
author = {Marc Aubreville and Jonathan Ganz and Jonas Ammeling and Taryn A. Donovan and Rutger Hj. Fick and Katharina Breininger and Christof A. Bertram},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_40},
doi = {10.1007/978-3-658-41657-7_40},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {189–195},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Ganz, Jonathan; Lipnik, Karoline; Ammeling, Jonas; Richter, Barbara; Puget, Chloé; Parlak, Eda; Diehl, Laura; Klopfleisch, Robert; Donovan, Taryn A.; Kiupel, Matti; Bertram, Christof A.; Breininger, Katharina; Aubreville, Marc
Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 226–231, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_deep_2023-1,
title = {Deep Learning-based Automatic Assessment of AgNOR-scores in Histopathology Images},
author = {Jonathan Ganz and Karoline Lipnik and Jonas Ammeling and Barbara Richter and Chloé Puget and Eda Parlak and Laura Diehl and Robert Klopfleisch and Taryn A. Donovan and Matti Kiupel and Christof A. Bertram and Katharina Breininger and Marc Aubreville},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_49},
doi = {10.1007/978-3-658-41657-7_49},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {226–231},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Lausser, Ludwig M.; Bertram, Christof A.; Klopfleisch, Robert; Aubreville, Marc
Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 116–121, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_limits_2023,
title = {Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation},
author = {Ludwig M. Lausser and Christof A. Bertram and Robert Klopfleisch and Marc Aubreville},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_27},
doi = {10.1007/978-3-658-41657-7_27},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {116–121},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Öttl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc
Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 206–211, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_multi-scanner_2023,
title = {Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset},
author = {Frauke Wilm and Marco Fragoso and Christof A. Bertram and Nikolas Stathonikos and Mathias Öttl and Jingna Qiu and Robert Klopfleisch and Andreas Maier and Katharina Breininger and Marc Aubreville},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_46},
doi = {10.1007/978-3-658-41657-7_46},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-02-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {206–211},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Wilm, Frauke; Ihling, Christian; Méhes, Gábor; Terracciano, Luigi; Puget, Chloé; Klopfleisch, Robert; Schüffler, Peter; Aubreville, Marc; Maier, Andreas; Mrowiec, Thomas; Breininger, Katharina
Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry Journal Article
In: Journal of Pathology Informatics, vol. 14, pp. 100301, 2023, ISSN: 21533539.
@article{wilm_pan-tumor_2023,
title = {Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry},
author = {Frauke Wilm and Christian Ihling and Gábor Méhes and Luigi Terracciano and Chloé Puget and Robert Klopfleisch and Peter Schüffler and Marc Aubreville and Andreas Maier and Thomas Mrowiec and Katharina Breininger},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2153353923001153},
doi = {10.1016/j.jpi.2023.100301},
issn = {21533539},
year = {2023},
date = {2023-02-01},
urldate = {2023-07-01},
journal = {Journal of Pathology Informatics},
volume = {14},
pages = {100301},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Utz, Jonas; Schlereth, Maja; Qiu, Jingna; Thies, Mareike; Wagner, Fabian; Brahim, Oumaima Ben; Gu, Mingxuan; Uderhardt, Stefan; Breininger, Katharina
McLabel: A Local Thresholding Tool for Efficient Semi-automatic Labelling of Cells in Fluorescence Microscopy Book Section
In: Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2023, pp. 82–87, Springer Fachmedien Wiesbaden, Wiesbaden, 2023, ISBN: 978-3-658-41656-0 978-3-658-41657-7, (Series Title: Informatik aktuell).
@incollection{deserno_mclabel_2023,
title = {McLabel: A Local Thresholding Tool for Efficient Semi-automatic Labelling of Cells in Fluorescence Microscopy},
author = {Jonas Utz and Maja Schlereth and Jingna Qiu and Mareike Thies and Fabian Wagner and Oumaima Ben Brahim and Mingxuan Gu and Stefan Uderhardt and Katharina Breininger},
editor = {Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-41657-7_20},
doi = {10.1007/978-3-658-41657-7_20},
isbn = {978-3-658-41656-0 978-3-658-41657-7},
year = {2023},
date = {2023-01-01},
urldate = {2024-02-21},
booktitle = {Bildverarbeitung für die Medizin 2023},
pages = {82–87},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Qiu, Jingna; Wilm, Frauke; Öttl, Mathias; Schlereth, Maja; Liu, Chang; Heimann, Tobias; Aubreville, Marc; Breininger, Katharina
Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation Book Section
In: Greenspan, Hayit; Madabhushi, Anant; Mousavi, Parvin; Salcudean, Septimiu; Duncan, James; Syeda-Mahmood, Tanveer; Taylor, Russell (Ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, vol. 14221, pp. 90–100, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-43894-3 978-3-031-43895-0, (Series Title: Lecture Notes in Computer Science).
@incollection{greenspan_adaptive_2023,
title = {Adaptive Region Selection for Active Learning in Whole Slide Image Semantic Segmentation},
author = {Jingna Qiu and Frauke Wilm and Mathias Öttl and Maja Schlereth and Chang Liu and Tobias Heimann and Marc Aubreville and Katharina Breininger},
editor = {Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor},
url = {https://link.springer.com/10.1007/978-3-031-43895-0_9},
doi = {10.1007/978-3-031-43895-0_9},
isbn = {978-3-031-43894-3 978-3-031-43895-0},
year = {2023},
date = {2023-01-01},
urldate = {2023-11-20},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2023},
volume = {14221},
pages = {90–100},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Stathonikos, Nikolas; Bertram, Christof A.; Klopfleisch, Robert; Hoeve, Natalie Ter; Ciompi, Francesco; Wilm, Frauke; Marzahl, Christian; Donovan, Taryn A.; Maier, Andreas; Breen, Jack; Ravikumar, Nishant; Chung, Youjin; Park, Jinah; Nateghi, Ramin; Pourakpour, Fattaneh; Fick, Rutger H. J.; Hadj, Saima Ben; Jahanifar, Mostafa; Shephard, Adam; Dexl, Jakob; Wittenberg, Thomas; Kondo, Satoshi; Lafarge, Maxime W.; Koelzer, Viktor H.; Liang, Jingtang; Wang, Yubo; Long, Xi; Liu, Jingxin; Razavi, Salar; Khademi, April; Yang, Sen; Wang, Xiyue; Erber, Ramona; Klang, Andrea; Lipnik, Karoline; Bolfa, Pompei; Dark, Michael J.; Wasinger, Gabriel; Veta, Mitko; Breininger, Katharina
Mitosis domain generalization in histopathology images — The MIDOG challenge Journal Article
In: Medical Image Analysis, vol. 84, pp. 102699, 2023, ISSN: 13618415.
@article{aubreville_mitosis_2023,
title = {Mitosis domain generalization in histopathology images — The MIDOG challenge},
author = {Marc Aubreville and Nikolas Stathonikos and Christof A. Bertram and Robert Klopfleisch and Natalie Ter Hoeve and Francesco Ciompi and Frauke Wilm and Christian Marzahl and Taryn A. Donovan and Andreas Maier and Jack Breen and Nishant Ravikumar and Youjin Chung and Jinah Park and Ramin Nateghi and Fattaneh Pourakpour and Rutger H. J. Fick and Saima Ben Hadj and Mostafa Jahanifar and Adam Shephard and Jakob Dexl and Thomas Wittenberg and Satoshi Kondo and Maxime W. Lafarge and Viktor H. Koelzer and Jingtang Liang and Yubo Wang and Xi Long and Jingxin Liu and Salar Razavi and April Khademi and Sen Yang and Xiyue Wang and Ramona Erber and Andrea Klang and Karoline Lipnik and Pompei Bolfa and Michael J. Dark and Gabriel Wasinger and Mitko Veta and Katharina Breininger},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1361841522003279},
doi = {10.1016/j.media.2022.102699},
issn = {13618415},
year = {2023},
date = {2023-01-01},
urldate = {2023-05-19},
journal = {Medical Image Analysis},
volume = {84},
pages = {102699},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pan, Zhaoya; Breininger, Katharina; Aubreville, Marc; Stelzle, Florian; Oetter, Nicolai; Maier, Andreas; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel; Sievert, Matti
Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging Journal Article
In: American Journal of Otolaryngology, vol. 44, no. 2, pp. 103779, 2023, ISSN: 01960709.
@article{pan_defining_2023,
title = {Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging},
author = {Zhaoya Pan and Katharina Breininger and Marc Aubreville and Florian Stelzle and Nicolai Oetter and Andreas Maier and Konstantinos Mantsopoulos and Heinrich Iro and Miguel Goncalves and Matti Sievert},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0196070922004069},
doi = {10.1016/j.amjoto.2022.103779},
issn = {01960709},
year = {2023},
date = {2023-01-01},
urldate = {2023-07-01},
journal = {American Journal of Otolaryngology},
volume = {44},
number = {2},
pages = {103779},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ammeling, Jonas; Wilm, Frauke; Ganz, Jonathan; Breininger, Katharina; Aubreville, Marc
Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge Book Section
In: Sheng, Bin; Aubreville, Marc (Ed.): Mitosis Domain Generalization and Diabetic Retinopathy Analysis, vol. 13597, pp. 201–205, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-33657-7 978-3-031-33658-4, (Series Title: Lecture Notes in Computer Science).
@incollection{sheng_reference_2023-1,
title = {Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge},
author = {Jonas Ammeling and Frauke Wilm and Jonathan Ganz and Katharina Breininger and Marc Aubreville},
editor = {Bin Sheng and Marc Aubreville},
url = {https://link.springer.com/10.1007/978-3-031-33658-4_19},
doi = {10.1007/978-3-031-33658-4_19},
isbn = {978-3-031-33657-7 978-3-031-33658-4},
year = {2023},
date = {2023-01-01},
urldate = {2023-07-02},
booktitle = {Mitosis Domain Generalization and Diabetic Retinopathy Analysis},
volume = {13597},
pages = {201–205},
publisher = {Springer Nature Switzerland},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2022
Sievert, Matti; Mantsopoulos, Konstantinos; Mueller, Sarina K.; Rupp, Robin; Eckstein, Markus; Stelzle, Florian; Oetter, Nicolai; Maier, Andreas; Aubreville, Marc; Iro, Heinrich; Goncalves, Miguel
In: Brazilian Journal of Otorhinolaryngology, vol. 88, pp. S26–S32, 2022, ISSN: 18088694.
@article{sievert_validation_2022,
title = {Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy},
author = {Matti Sievert and Konstantinos Mantsopoulos and Sarina K. Mueller and Robin Rupp and Markus Eckstein and Florian Stelzle and Nicolai Oetter and Andreas Maier and Marc Aubreville and Heinrich Iro and Miguel Goncalves},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1808869421001245},
doi = {10.1016/j.bjorl.2021.06.002},
issn = {18088694},
year = {2022},
date = {2022-11-01},
urldate = {2023-06-30},
journal = {Brazilian Journal of Otorhinolaryngology},
volume = {88},
pages = {S26–S32},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Wilm, Frauke; Fragoso, Marco; Marzahl, Christian; Qiu, Jingna; Puget, Chloé; Diehl, Laura; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc
Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset Journal Article
In: Scientific Data, vol. 9, no. 1, pp. 588, 2022, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{wilm_pan-tumor_2022,
title = {Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset},
author = {Frauke Wilm and Marco Fragoso and Christian Marzahl and Jingna Qiu and Chloé Puget and Laura Diehl and Christof A. Bertram and Robert Klopfleisch and Andreas Maier and Katharina Breininger and Marc Aubreville},
url = {https://www.nature.com/articles/s41597-022-01692-w},
doi = {10.1038/s41597-022-01692-w},
issn = {2052-4463},
year = {2022},
date = {2022-09-01},
urldate = {2023-06-30},
journal = {Scientific Data},
volume = {9},
number = {1},
pages = {588},
abstract = {Abstract
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.
Sievert, Matti; Aubreville, Marc; Gostian, Antoniu-Oreste; Mantsopoulos, Konstantinos; Koch, Michael; Mueller, Sarina Katrin; Eckstein, Markus; Rupp, Robin; Stelzle, Florian; Oetter, Nicolai; Maier, Andreas; Iro, Heinrich; Goncalves, Miguel
Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma Journal Article
In: European Archives of Oto-Rhino-Laryngology, vol. 279, no. 8, pp. 4147–4156, 2022, ISSN: 0937-4477, 1434-4726.
Abstract | Links | BibTeX | Tags:
@article{sievert_validity_2022,
title = {Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma},
author = {Matti Sievert and Marc Aubreville and Antoniu-Oreste Gostian and Konstantinos Mantsopoulos and Michael Koch and Sarina Katrin Mueller and Markus Eckstein and Robin Rupp and Florian Stelzle and Nicolai Oetter and Andreas Maier and Heinrich Iro and Miguel Goncalves},
url = {https://link.springer.com/10.1007/s00405-022-07304-y},
doi = {10.1007/s00405-022-07304-y},
issn = {0937-4477, 1434-4726},
year = {2022},
date = {2022-08-01},
urldate = {2026-02-17},
journal = {European Archives of Oto-Rhino-Laryngology},
volume = {279},
number = {8},
pages = {4147–4156},
abstract = {Abstract
Purpose
Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE.
Materials and methods
We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity.
Results
Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (
P
< 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (
P
< 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis.
Conclusions
SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE.
Materials and methods
We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity.
Results
Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (
P
< 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (
P
< 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis.
Conclusions
SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context.
Marzahl, Christian; Hill, Jenny; Stayt, Jason; Bienzle, Dorothee; Welker, Lutz; Wilm, Frauke; Voigt, Jörn; Aubreville, Marc; Maier, Andreas; Klopfleisch, Robert; Breininger, Katharina; Bertram, Christof A.
Inter-species cell detection – datasets on pulmonary hemosiderophages in equine, human and feline specimens Journal Article
In: Scientific Data, vol. 9, no. 1, pp. 269, 2022, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{marzahl_inter-species_2022,
title = {Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens},
author = {Christian Marzahl and Jenny Hill and Jason Stayt and Dorothee Bienzle and Lutz Welker and Frauke Wilm and Jörn Voigt and Marc Aubreville and Andreas Maier and Robert Klopfleisch and Katharina Breininger and Christof A. Bertram},
url = {https://www.nature.com/articles/s41597-022-01389-0},
doi = {10.1038/s41597-022-01389-0},
issn = {2052-4463},
year = {2022},
date = {2022-06-01},
urldate = {2023-07-01},
journal = {Scientific Data},
volume = {9},
number = {1},
pages = {269},
abstract = {Abstract
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered.
Sievert, Matti; Eckstein, Markus; Mantsopoulos, Konstantinos; Mueller, Sarina K.; Stelzle, Florian; Aubreville, Marc; Oetter, Nicolai; Maier, Andreas; Iro, Heinrich; Goncalves, Miguel
In: European Archives of Oto-Rhino-Laryngology, vol. 279, no. 4, pp. 2029–2037, 2022, ISSN: 0937-4477, 1434-4726.
Abstract | Links | BibTeX | Tags:
@article{sievert_impact_2022,
title = {Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma},
author = {Matti Sievert and Markus Eckstein and Konstantinos Mantsopoulos and Sarina K. Mueller and Florian Stelzle and Marc Aubreville and Nicolai Oetter and Andreas Maier and Heinrich Iro and Miguel Goncalves},
url = {https://link.springer.com/10.1007/s00405-021-06954-8},
doi = {10.1007/s00405-021-06954-8},
issn = {0937-4477, 1434-4726},
year = {2022},
date = {2022-04-01},
urldate = {2023-06-30},
journal = {European Archives of Oto-Rhino-Laryngology},
volume = {279},
number = {4},
pages = {2029–2037},
abstract = {Abstract
Purpose
Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE.
Materials and methods
We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC.
Results
In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%;
P
< .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%;
P
< .001), and increased capillary leakage (40.7% vs. 2.5%;
P
< .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture.
Conclusion
Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE.
Materials and methods
We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC.
Results
In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%;
P
< .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%;
P
< .001), and increased capillary leakage (40.7% vs. 2.5%;
P
< .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture.
Conclusion
Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination.
Bertram, Christof A.; Aubreville, Marc; Donovan, Taryn A.; Bartel, Alexander; Wilm, Frauke; Marzahl, Christian; Assenmacher, Charles-Antoine; Becker, Kathrin; Bennett, Mark; Corner, Sarah; Cossic, Brieuc; Denk, Daniela; Dettwiler, Martina; Gonzalez, Beatriz Garcia; Gurtner, Corinne; Haverkamp, Ann-Kathrin; Heier, Annabelle; Lehmbecker, Annika; Merz, Sophie; Noland, Erica L.; Plog, Stephanie; Schmidt, Anja; Sebastian, Franziska; Sledge, Dodd G.; Smedley, Rebecca C.; Tecilla, Marco; Thaiwong, Tuddow; Fuchs-Baumgartinger, Andrea; Meuten, Donald J.; Breininger, Katharina; Kiupel, Matti; Maier, Andreas; Klopfleisch, Robert
Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy Journal Article
In: Veterinary Pathology, vol. 59, no. 2, pp. 211–226, 2022, ISSN: 0300-9858, 1544-2217.
Abstract | Links | BibTeX | Tags:
@article{bertram_computer-assisted_2022,
title = {Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy},
author = {Christof A. Bertram and Marc Aubreville and Taryn A. Donovan and Alexander Bartel and Frauke Wilm and Christian Marzahl and Charles-Antoine Assenmacher and Kathrin Becker and Mark Bennett and Sarah Corner and Brieuc Cossic and Daniela Denk and Martina Dettwiler and Beatriz Garcia Gonzalez and Corinne Gurtner and Ann-Kathrin Haverkamp and Annabelle Heier and Annika Lehmbecker and Sophie Merz and Erica L. Noland and Stephanie Plog and Anja Schmidt and Franziska Sebastian and Dodd G. Sledge and Rebecca C. Smedley and Marco Tecilla and Tuddow Thaiwong and Andrea Fuchs-Baumgartinger and Donald J. Meuten and Katharina Breininger and Matti Kiupel and Andreas Maier and Robert Klopfleisch},
url = {http://journals.sagepub.com/doi/10.1177/03009858211067478},
doi = {10.1177/03009858211067478},
issn = {0300-9858, 1544-2217},
year = {2022},
date = {2022-03-01},
urldate = {2023-06-30},
journal = {Veterinary Pathology},
volume = {59},
number = {2},
pages = {211–226},
abstract = {The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Ganz, Jonathan; Bertram, Christof A.; Klopfleisch, Robert; Jabari, Samir; Breininger, Katharina; Aubreville, Marc
Classification of visibility in multi-stain microscopy images Proceedings Article
In: Medical Imaging with Deep Learning 2022, Zurich, 2022.
BibTeX | Tags:
@inproceedings{ganz_classification_2022,
title = {Classification of visibility in multi-stain microscopy images},
author = {Jonathan Ganz and Christof A. Bertram and Robert Klopfleisch and Samir Jabari and Katharina Breininger and Marc Aubreville},
year = {2022},
date = {2022-01-01},
booktitle = {Medical Imaging with Deep Learning 2022},
address = {Zurich},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Öttl, Mathias; Mönius, Jana; Marzahl, Christian; Rübner, Matthias; Geppert, Carol I.; Hartmann, Arndt; Beckmann, Matthias W.; Fasching, Peter; Maier, Andreas; Erber, Ramona; Breininger, Katharina
Superpixel Pre-segmentation of HER2 Slides for Efficient Annotation Book Section
In: Maier-Hein, Klaus; Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2022, pp. 254–259, Springer Fachmedien Wiesbaden, Wiesbaden, 2022, ISBN: 978-3-658-36931-6 978-3-658-36932-3, (Series Title: Informatik aktuell).
@incollection{maier-hein_superpixel_2022,
title = {Superpixel Pre-segmentation of HER2 Slides for Efficient Annotation},
author = {Mathias Öttl and Jana Mönius and Christian Marzahl and Matthias Rübner and Carol I. Geppert and Arndt Hartmann and Matthias W. Beckmann and Peter Fasching and Andreas Maier and Ramona Erber and Katharina Breininger},
editor = {Klaus Maier-Hein and Thomas M. Deserno and Heinz Handels and Andreas Maier and Christoph Palm and Thomas Tolxdorff},
url = {https://link.springer.com/10.1007/978-3-658-36932-3_54},
doi = {10.1007/978-3-658-36932-3_54},
isbn = {978-3-658-36931-6 978-3-658-36932-3},
year = {2022},
date = {2022-01-01},
urldate = {2023-07-01},
booktitle = {Bildverarbeitung für die Medizin 2022},
pages = {254–259},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Wilm, Frauke; Marzahl, Christian; Breininger, Katharina; Aubreville, Marc
Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge Book Section
In: Aubreville, Marc; Zimmerer, David; Heinrich, Mattias (Ed.): Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, vol. 13166, pp. 5–13, Springer International Publishing, Cham, 2022, ISBN: 978-3-030-97280-6 978-3-030-97281-3, (Series Title: Lecture Notes in Computer Science).
@incollection{aubreville_domain_2022,
title = {Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge},
author = {Frauke Wilm and Christian Marzahl and Katharina Breininger and Marc Aubreville},
editor = {Marc Aubreville and David Zimmerer and Mattias Heinrich},
url = {https://link.springer.com/10.1007/978-3-030-97281-3_1},
doi = {10.1007/978-3-030-97281-3_1},
isbn = {978-3-030-97280-6 978-3-030-97281-3},
year = {2022},
date = {2022-01-01},
urldate = {2023-06-30},
booktitle = {Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis},
volume = {13166},
pages = {5–13},
publisher = {Springer International Publishing},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2021
Sievert, Matti; Stelzle, Florian; Aubreville, Marc; Mueller, Sarina K.; Eckstein, Markus; Oetter, Nicolai; Maier, Andreas; Mantsopoulos, Konstantinos; Iro, Heinrich; Goncalves, Miguel
Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study Journal Article
In: European Archives of Oto-Rhino-Laryngology, vol. 278, no. 11, pp. 4433–4439, 2021, ISSN: 0937-4477, 1434-4726.
Abstract | Links | BibTeX | Tags:
@article{sievert_intraoperative_2021,
title = {Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study},
author = {Matti Sievert and Florian Stelzle and Marc Aubreville and Sarina K. Mueller and Markus Eckstein and Nicolai Oetter and Andreas Maier and Konstantinos Mantsopoulos and Heinrich Iro and Miguel Goncalves},
url = {https://link.springer.com/10.1007/s00405-021-06659-y},
doi = {10.1007/s00405-021-06659-y},
issn = {0937-4477, 1434-4726},
year = {2021},
date = {2021-11-01},
urldate = {2023-06-30},
journal = {European Archives of Oto-Rhino-Laryngology},
volume = {278},
number = {11},
pages = {4433–4439},
abstract = {Abstract
Purpose
This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery.
Methods
We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results.
Results
Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and
κ
-value of 0.60.
Conclusion
CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purpose
This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery.
Methods
We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results.
Results
Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and
κ
-value of 0.60.
Conclusion
CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.
Theelke, Luisa; Wilm, Frauke; Marzahl, Christian; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina
Iterative Cross-Scanner Registration for Whole Slide Images Proceedings Article
In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 582–590, IEEE, Montreal, BC, Canada, 2021, ISBN: 978-1-66540-191-3.
@inproceedings{theelke_iterative_2021,
title = {Iterative Cross-Scanner Registration for Whole Slide Images},
author = {Luisa Theelke and Frauke Wilm and Christian Marzahl and Christof A. Bertram and Robert Klopfleisch and Andreas Maier and Marc Aubreville and Katharina Breininger},
url = {https://ieeexplore.ieee.org/document/9607816/},
doi = {10.1109/ICCVW54120.2021.00071},
isbn = {978-1-66540-191-3},
year = {2021},
date = {2021-10-01},
urldate = {2023-06-30},
booktitle = {2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
pages = {582–590},
publisher = {IEEE},
address = {Montreal, BC, Canada},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Meuten, Donald J.; Moore, Frances M.; Donovan, Taryn A.; Bertram, Christof A.; Klopfleisch, Robert; Foster, Robert A.; Smedley, Rebecca C.; Dark, Michael J.; Milovancev, Milan; Stromberg, Paul; Williams, Bruce H.; Aubreville, Marc; Avallone, Giancarlo; Bolfa, Pompei; Cullen, John; Dennis, Michelle M.; Goldschmidt, Michael; Luong, Richard; Miller, Andrew D.; Miller, Margaret A.; Munday, John S.; Roccabianca, Paola; Salas, Elisa N.; Schulman, F. Yvonne; Laufer-Amorim, Renee; Asakawa, Midori G.; Craig, Linden; Dervisis, Nick; Esplin, D. Glen; George, Jeanne W.; Hauck, Marlene; Kagawa, Yumiko; Kiupel, Matti; Linder, Keith; Meichner, Kristina; Marconato, Laura; Oblak, Michelle L.; Santos, Renato L.; Simpson, R. Mark; Tvedten, Harold; Whitley, Derick
International Guidelines for Veterinary Tumor Pathology: A Call to Action Journal Article
In: Veterinary Pathology, vol. 58, no. 5, pp. 766–794, 2021, ISSN: 0300-9858, 1544-2217.
Abstract | Links | BibTeX | Tags:
@article{meuten_international_2021,
title = {International Guidelines for Veterinary Tumor Pathology: A Call to Action},
author = {Donald J. Meuten and Frances M. Moore and Taryn A. Donovan and Christof A. Bertram and Robert Klopfleisch and Robert A. Foster and Rebecca C. Smedley and Michael J. Dark and Milan Milovancev and Paul Stromberg and Bruce H. Williams and Marc Aubreville and Giancarlo Avallone and Pompei Bolfa and John Cullen and Michelle M. Dennis and Michael Goldschmidt and Richard Luong and Andrew D. Miller and Margaret A. Miller and John S. Munday and Paola Roccabianca and Elisa N. Salas and F. Yvonne Schulman and Renee Laufer-Amorim and Midori G. Asakawa and Linden Craig and Nick Dervisis and D. Glen Esplin and Jeanne W. George and Marlene Hauck and Yumiko Kagawa and Matti Kiupel and Keith Linder and Kristina Meichner and Laura Marconato and Michelle L. Oblak and Renato L. Santos and R. Mark Simpson and Harold Tvedten and Derick Whitley},
url = {http://journals.sagepub.com/doi/10.1177/03009858211013712},
doi = {10.1177/03009858211013712},
issn = {0300-9858, 1544-2217},
year = {2021},
date = {2021-09-01},
urldate = {2023-06-30},
journal = {Veterinary Pathology},
volume = {58},
number = {5},
pages = {766–794},
abstract = {Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as “living documents” on a website ( www.vetcancerprotocols.org ), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sievert, Matti; Oetter, Nicolai; Aubreville, Marc; Stelzle, Florian; Maier, Andreas; Eckstein, Markus; Mantsopoulos, Konstantinos; Gostian, Antoniu-Oreste; Mueller, Sarina K; Koch, Michael; Agaimy, Abbas; Iro, Heinrich; Goncalves, Miguel
Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study Journal Article
In: Auris Nasus Larynx, vol. 48, no. 4, pp. 764–769, 2021, ISSN: 03858146.
@article{sievert_feasibility_2021,
title = {Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy: A pilot study},
author = {Matti Sievert and Nicolai Oetter and Marc Aubreville and Florian Stelzle and Andreas Maier and Markus Eckstein and Konstantinos Mantsopoulos and Antoniu-Oreste Gostian and Sarina K Mueller and Michael Koch and Abbas Agaimy and Heinrich Iro and Miguel Goncalves},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0385814621000237},
doi = {10.1016/j.anl.2021.01.005},
issn = {03858146},
year = {2021},
date = {2021-08-01},
urldate = {2023-06-30},
journal = {Auris Nasus Larynx},
volume = {48},
number = {4},
pages = {764–769},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Donovan, Taryn A.; Moore, Frances M.; Bertram, Christof A.; Luong, Richard; Bolfa, Pompei; Klopfleisch, Robert; Tvedten, Harold; Salas, Elisa N.; Whitley, Derick B.; Aubreville, Marc; Meuten, Donald J.
Mitotic Figures—Normal, Atypical, and Imposters: A Guide to Identification Journal Article
In: Veterinary Pathology, vol. 58, no. 2, pp. 243–257, 2021, ISSN: 0300-9858, 1544-2217.
Abstract | Links | BibTeX | Tags:
@article{donovan_mitotic_2021,
title = {Mitotic Figures—Normal, Atypical, and Imposters: A Guide to Identification},
author = {Taryn A. Donovan and Frances M. Moore and Christof A. Bertram and Richard Luong and Pompei Bolfa and Robert Klopfleisch and Harold Tvedten and Elisa N. Salas and Derick B. Whitley and Marc Aubreville and Donald J. Meuten},
url = {http://journals.sagepub.com/doi/10.1177/0300985820980049},
doi = {10.1177/0300985820980049},
issn = {0300-9858, 1544-2217},
year = {2021},
date = {2021-03-01},
urldate = {2023-06-30},
journal = {Veterinary Pathology},
volume = {58},
number = {2},
pages = {243–257},
abstract = {Counting mitotic figures (MF) in hematoxylin and eosin–stained histologic sections is an integral part of the diagnostic pathologist’s tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm
2
). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2
). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.
Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Maier, Jennifer; Bergler, Christian; Kröger, Christine; Voigt, Jörn; Breininger, Katharina; Klopfleisch, Robert; Maier, Andreas
EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control Journal Article
In: Scientific Reports, vol. 11, no. 1, pp. 4343, 2021, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{marzahl_exact_2021,
title = {EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control},
author = {Christian Marzahl and Marc Aubreville and Christof A. Bertram and Jennifer Maier and Christian Bergler and Christine Kröger and Jörn Voigt and Katharina Breininger and Robert Klopfleisch and Andreas Maier},
url = {https://www.nature.com/articles/s41598-021-83827-4},
doi = {10.1038/s41598-021-83827-4},
issn = {2045-2322},
year = {2021},
date = {2021-02-01},
urldate = {2023-06-30},
journal = {Scientific Reports},
volume = {11},
number = {1},
pages = {4343},
abstract = {Abstract
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering.
Ganz, Jonathan; Kirsch, Tobias; Hoffmann, Lucas; Bertram, Christof A; Hoffmann, Christoph; Maier, Andreas; Breininger, Katharina; Blümcke, Ingmar; Jabari, Samir; Aubreville, Marc
Automatic and explainable grading of meningiomas from histopathology images Proceedings Article
In: pp. 69–80, PMLR, 2021, ISBN: 2640-3498.
@inproceedings{ganz_automatic_2021,
title = {Automatic and explainable grading of meningiomas from histopathology images},
author = {Jonathan Ganz and Tobias Kirsch and Lucas Hoffmann and Christof A Bertram and Christoph Hoffmann and Andreas Maier and Katharina Breininger and Ingmar Blümcke and Samir Jabari and Marc Aubreville},
url = {https://proceedings.mlr.press/v156/ganz21a/ganz21a.pdf},
isbn = {2640-3498},
year = {2021},
date = {2021-01-01},
pages = {69–80},
publisher = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Marzahl, Christian; Wilm, Frauke; Tharun, Lars; Perner, Sven; Bertram, Christof A; Kröger, Christine; Voigt, Jörn; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc
Robust quad-tree based registration on whole slide images Proceedings Article
In: pp. 181–190, PMLR, 2021, ISBN: 2640-3498.
BibTeX | Tags:
@inproceedings{marzahl_robust_2021,
title = {Robust quad-tree based registration on whole slide images},
author = {Christian Marzahl and Frauke Wilm and Lars Tharun and Sven Perner and Christof A Bertram and Christine Kröger and Jörn Voigt and Robert Klopfleisch and Andreas Maier and Marc Aubreville},
isbn = {2640-3498},
year = {2021},
date = {2021-01-01},
pages = {181–190},
publisher = {PMLR},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Wilm, Frauke; Bertram, Christof A.; Marzahl, Christian; Bartel, Alexander; Donovan, Taryn A.; Assenmacher, Charles-Antoine; Becker, Kathrin; Bennett, Mark; Corner, Sarah; Cossic, Brieuc; Denk, Daniela; Dettwiler, Martina; Gonzalez, Beatriz Garcia; Gurtner, Corinne; Heier, Annabelle; Lehmbecker, Annika; Merz, Sophie; Plog, Stephanie; Schmidt, Anja; Sebastian, Franziska; Smedley, Rebecca C.; Tecilla, Marco; Thaiwong, Tuddow; Breininger, Katharina; Kiupel, Matti; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc
Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment Book Section
In: Palm, Christoph; Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2021, pp. 241–246, Springer Fachmedien Wiesbaden, Wiesbaden, 2021, ISBN: 978-3-658-33197-9 978-3-658-33198-6, (Series Title: Informatik aktuell).
@incollection{palm_influence_2021,
title = {Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment},
author = {Frauke Wilm and Christof A. Bertram and Christian Marzahl and Alexander Bartel and Taryn A. Donovan and Charles-Antoine Assenmacher and Kathrin Becker and Mark Bennett and Sarah Corner and Brieuc Cossic and Daniela Denk and Martina Dettwiler and Beatriz Garcia Gonzalez and Corinne Gurtner and Annabelle Heier and Annika Lehmbecker and Sophie Merz and Stephanie Plog and Anja Schmidt and Franziska Sebastian and Rebecca C. Smedley and Marco Tecilla and Tuddow Thaiwong and Katharina Breininger and Matti Kiupel and Andreas Maier and Robert Klopfleisch and Marc Aubreville},
editor = {Christoph Palm and Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-658-33198-6_56},
doi = {10.1007/978-3-658-33198-6_56},
isbn = {978-3-658-33197-9 978-3-658-33198-6},
year = {2021},
date = {2021-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2021},
pages = {241–246},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Marzahl, Christian; Bertram, Christof A.; Wilm, Frauke; Voigt, Jörn; Barton, Ann K.; Klopfleisch, Robert; Breininger, Katharina; Maier, Andreas; Aubreville, Marc
Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT Book Section
In: Palm, Christoph; Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2021, pp. 147–152, Springer Fachmedien Wiesbaden, Wiesbaden, 2021, ISBN: 978-3-658-33197-9 978-3-658-33198-6, (Series Title: Informatik aktuell).
@incollection{palm_cell_2021,
title = {Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT},
author = {Christian Marzahl and Christof A. Bertram and Frauke Wilm and Jörn Voigt and Ann K. Barton and Robert Klopfleisch and Katharina Breininger and Andreas Maier and Marc Aubreville},
editor = {Christoph Palm and Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-658-33198-6_36},
doi = {10.1007/978-3-658-33198-6_36},
isbn = {978-3-658-33197-9 978-3-658-33198-6},
year = {2021},
date = {2021-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2021},
pages = {147–152},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Bertram, Christof A.; Donovan, Taryn A.; Tecilla, Marco; Bartenschlager, Florian; Fragoso, Marco; Wilm, Frauke; Marzahl, Christian; Breininger, Katharina; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc
Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors Book Section
In: Palm, Christoph; Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2021, pp. 134–139, Springer Fachmedien Wiesbaden, Wiesbaden, 2021, ISBN: 978-3-658-33197-9 978-3-658-33198-6, (Series Title: Informatik aktuell).
@incollection{palm_dataset_2021,
title = {Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors},
author = {Christof A. Bertram and Taryn A. Donovan and Marco Tecilla and Florian Bartenschlager and Marco Fragoso and Frauke Wilm and Christian Marzahl and Katharina Breininger and Andreas Maier and Robert Klopfleisch and Marc Aubreville},
editor = {Christoph Palm and Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus Maier-Hein and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-658-33198-6_33},
doi = {10.1007/978-3-658-33198-6_33},
isbn = {978-3-658-33197-9 978-3-658-33198-6},
year = {2021},
date = {2021-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2021},
pages = {134–139},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2020
Aubreville, Marc; Bertram, Christof A.; Donovan, Taryn A.; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert
A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research Journal Article
In: Scientific Data, vol. 7, no. 1, pp. 417, 2020, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@article{aubreville_completely_2020,
title = {A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research},
author = {Marc Aubreville and Christof A. Bertram and Taryn A. Donovan and Christian Marzahl and Andreas Maier and Robert Klopfleisch},
url = {https://www.nature.com/articles/s41597-020-00756-z},
doi = {10.1038/s41597-020-00756-z},
issn = {2052-4463},
year = {2020},
date = {2020-11-01},
urldate = {2023-06-30},
journal = {Scientific Data},
volume = {7},
number = {1},
pages = {417},
abstract = {Abstract
Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset.
Aubreville, Marc; Bertram, Christof A.; Marzahl, Christian; Gurtner, Corinne; Dettwiler, Martina; Schmidt, Anja; Bartenschlager, Florian; Merz, Sophie; Fragoso, Marco; Kershaw, Olivia; Klopfleisch, Robert; Maier, Andreas
Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region Journal Article
In: Scientific Reports, vol. 10, no. 1, pp. 16447, 2020, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{aubreville_deep_2020,
title = {Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region},
author = {Marc Aubreville and Christof A. Bertram and Christian Marzahl and Corinne Gurtner and Martina Dettwiler and Anja Schmidt and Florian Bartenschlager and Sophie Merz and Marco Fragoso and Olivia Kershaw and Robert Klopfleisch and Andreas Maier},
url = {https://www.nature.com/articles/s41598-020-73246-2},
doi = {10.1038/s41598-020-73246-2},
issn = {2045-2322},
year = {2020},
date = {2020-10-01},
urldate = {2023-06-30},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {16447},
abstract = {Abstract
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.
Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Stayt, Jason; Jasensky, Anne-Katherine; Bartenschlager, Florian; Fragoso-Garcia, Marco; Barton, Ann K.; Elsemann, Svenja; Jabari, Samir; Krauth, Jens; Madhu, Prathmesh; Voigt, Jörn; Hill, Jenny; Klopfleisch, Robert; Maier, Andreas
Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides Journal Article
In: Scientific Reports, vol. 10, no. 1, pp. 9795, 2020, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{marzahl_deep_2020,
title = {Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides},
author = {Christian Marzahl and Marc Aubreville and Christof A. Bertram and Jason Stayt and Anne-Katherine Jasensky and Florian Bartenschlager and Marco Fragoso-Garcia and Ann K. Barton and Svenja Elsemann and Samir Jabari and Jens Krauth and Prathmesh Madhu and Jörn Voigt and Jenny Hill and Robert Klopfleisch and Andreas Maier},
url = {https://www.nature.com/articles/s41598-020-65958-2},
doi = {10.1038/s41598-020-65958-2},
issn = {2045-2322},
year = {2020},
date = {2020-08-01},
urldate = {2023-06-30},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {9795},
abstract = {Abstract Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aubreville, Marc
Computer-Aided Tumor Diagnosis of Microscopy Images PhD Thesis
Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020.
@phdthesis{aubreville_computer-aided_2020,
title = {Computer-Aided Tumor Diagnosis of Microscopy Images},
author = {Marc Aubreville},
url = {https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-137551},
year = {2020},
date = {2020-06-01},
address = {Erlangen},
school = {Friedrich-Alexander-Universität Erlangen-Nürnberg},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Schroter, Hendrick; Rosenkranz, Tobias; Escalante-B, Alberto N.; Aubreville, Marc; Maier, Andreas
CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding Proceedings Article
In: ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6949–6953, IEEE, Barcelona, Spain, 2020, ISBN: 978-1-5090-6631-5.
@inproceedings{schroter_clcnet_2020,
title = {CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding},
author = {Hendrick Schroter and Tobias Rosenkranz and Alberto N. Escalante-B and Marc Aubreville and Andreas Maier},
url = {https://ieeexplore.ieee.org/document/9053563/},
doi = {10.1109/ICASSP40776.2020.9053563},
isbn = {978-1-5090-6631-5},
year = {2020},
date = {2020-05-01},
urldate = {2020-05-01},
booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {6949–6953},
publisher = {IEEE},
address = {Barcelona, Spain},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}