@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@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}

}

@article{bertram_computerized_2020,

title = {Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent},

author = {Christof A. Bertram and Marc Aubreville and Corinne Gurtner and Alexander Bartel and Sarah M. Corner and Martina Dettwiler and Olivia Kershaw and Erica L. Noland and Anja Schmidt and Dodd G. Sledge and Rebecca C. Smedley and Tuddow Thaiwong and Matti Kiupel and Andreas Maier and Robert Klopfleisch},

url = {http://journals.sagepub.com/doi/10.1177/0300985819890686},

doi = {10.1177/0300985819890686},

issn = {0300-9858, 1544-2217},

year  = {2020},

date = {2020-03-01},

urldate = {2023-06-30},

journal = {Veterinary Pathology},

volume = {57},

number = {2},

pages = {214–226},

abstract = {Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bertram_mitotic_2020,

title = {Mitotic Count in Canine Cutaneous Mast Cell Tumours – Not Accurate but Reproducible},

author = {Christof A. Bertram and Marc Aubreville and Corinna Gurtner and Alexander Bartel and S. M. Corner and M. Dettwiler and O. Kershaw and E. L. Noland and A. Schmidt and D. G. Sledge and R. C. Smedley and T. Thaiwong and Matti Kiupel and A. Maier and Robert Klopfleisch},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0021997519303111},

doi = {10.1016/j.jcpa.2019.10.015},

issn = {00219975},

year  = {2020},

date = {2020-01-01},

urldate = {2023-06-30},

journal = {Journal of Comparative Pathology},

volume = {174},

pages = {143},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{goncalves_probe-based_2019,

title = {Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds},

author = {Miguel Goncalves and Marc Aubreville and Sarina K Mueller and Matti Sievert and Andreas Maier and Heinrich Iro and Christopher Bohr},

url = {https://www.actaitalica.it/article/view/117},

doi = {10.14639/0392-100X-2121},

issn = {1827-675X},

year  = {2019},

date = {2019-12-01},

urldate = {2023-06-30},

journal = {Acta Otorhinolaryngologica Italica},

volume = {39},

number = {6},

pages = {389–395},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bertram_large-scale_2019,

title = {A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor},

author = {Christof A. Bertram and Marc Aubreville and Christian Marzahl and Andreas Maier and Robert Klopfleisch},

url = {https://www.nature.com/articles/s41597-019-0290-4},

doi = {10.1038/s41597-019-0290-4},

issn = {2052-4463},

year  = {2019},

date = {2019-11-01},

urldate = {2023-06-30},

journal = {Scientific Data},

volume = {6},

number = {1},

pages = {274},

abstract = {Abstract 

 We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}