Prof. Dr.
Marc Aubreville
I’ve received my Ph.D (Dr.-Ing.) from Friedrich-Alexander-Universität Erlangen-Nürnberg and my M. Sc. (Dipl.-Ing.) from Karlsruhe Institute of Technology. My vision is to utilize the power of artificial intelligence algorithms and scale them to clinical applications. With 10+ years of industry experience, I know what it takes to create a great medical product.
You can reach me at marc.aubreville@hs-flensburg.de
Publications:
2022
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}
}
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 = {},
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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 = {},
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}
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},
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}
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}
}
Bertram, Christof A.; Aubreville, Marc; Gurtner, Corinne; Bartel, Alexander; Corner, Sarah M.; Dettwiler, Martina; Kershaw, Olivia; Noland, Erica L.; Schmidt, Anja; Sledge, Dodd G.; Smedley, Rebecca C.; Thaiwong, Tuddow; Kiupel, Matti; Maier, Andreas; Klopfleisch, Robert
Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent Journal Article
In: Veterinary Pathology, vol. 57, no. 2, pp. 214–226, 2020, ISSN: 0300-9858, 1544-2217.
Abstract | Links | BibTeX | Tags:
@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},
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}
Marzahl, Christian; Bertram, Christof A.; Aubreville, Marc; Petrick, Anne; Weiler, Kristina; Gläsel, Agnes C.; Fragoso, Marco; Merz, Sophie; Bartenschlager, Florian; Hoppe, Judith; Langenhagen, Alina; Jasensky, Anne-Katherine; Voigt, Jörn; Klopfleisch, Robert; Maier, Andreas
Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides Book Section
In: Martel, Anne L.; Abolmaesumi, Purang; Stoyanov, Danail; Mateus, Diana; Zuluaga, Maria A.; Zhou, S. Kevin; Racoceanu, Daniel; Joskowicz, Leo (Ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, vol. 12261, pp. 24–32, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-59709-2 978-3-030-59710-8, (Series Title: Lecture Notes in Computer Science).
@incollection{martel_are_2020,
title = {Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides},
author = {Christian Marzahl and Christof A. Bertram and Marc Aubreville and Anne Petrick and Kristina Weiler and Agnes C. Gläsel and Marco Fragoso and Sophie Merz and Florian Bartenschlager and Judith Hoppe and Alina Langenhagen and Anne-Katherine Jasensky and Jörn Voigt and Robert Klopfleisch and Andreas Maier},
editor = {Anne L. Martel and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Maria A. Zuluaga and S. Kevin Zhou and Daniel Racoceanu and Leo Joskowicz},
url = {https://link.springer.com/10.1007/978-3-030-59710-8_3},
doi = {10.1007/978-3-030-59710-8_3},
isbn = {978-3-030-59709-2 978-3-030-59710-8},
year = {2020},
date = {2020-01-01},
urldate = {2023-06-30},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2020},
volume = {12261},
pages = {24–32},
publisher = {Springer International Publishing},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Marzahl, Christian; Aubreville, Marc; Bertram, Christof A.; Gerlach, Stefan; Maier, Jennifer; Voigt, Jörn; Hill, Jenny; Klopfleisch, Robert; Maier, Andreas
Is Crowd-Algorithm Collaboration an Advanced Alternative to Crowd-Sourcing on Cytology Slides? Book Section
In: Tolxdorff, Thomas; Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus H.; Palm, Christoph (Ed.): Bildverarbeitung für die Medizin 2020, pp. 26–31, Springer Fachmedien Wiesbaden, Wiesbaden, 2020, ISBN: 978-3-658-29266-9 978-3-658-29267-6, (Series Title: Informatik aktuell).
@incollection{tolxdorff_is_2020,
title = {Is Crowd-Algorithm Collaboration an Advanced Alternative to Crowd-Sourcing on Cytology Slides?},
author = {Christian Marzahl and Marc Aubreville and Christof A. Bertram and Stefan Gerlach and Jennifer Maier and Jörn Voigt and Jenny Hill and Robert Klopfleisch and Andreas Maier},
editor = {Thomas Tolxdorff and Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus H. Maier-Hein and Christoph Palm},
url = {http://link.springer.com/10.1007/978-3-658-29267-6_5},
doi = {10.1007/978-3-658-29267-6_5},
isbn = {978-3-658-29266-9 978-3-658-29267-6},
year = {2020},
date = {2020-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2020},
pages = {26–31},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Bertram, Christof A.; Veta, Mitko; Marzahl, Christian; Stathonikos, Nikolas; Maier, Andreas; Klopfleisch, Robert; Aubreville, Marc
In: Cardoso, Jaime; Nguyen, Hien Van; Heller, Nicholas; Abreu, Pedro Henriques; Isgum, Ivana; Silva, Wilson; Cruz, Ricardo; Amorim, Jose Pereira; Patel, Vishal; Roysam, Badri; Zhou, Kevin; Jiang, Steve; Le, Ngan; Luu, Khoa; Sznitman, Raphael; Cheplygina, Veronika; Mateus, Diana; Trucco, Emanuele; Abbasi, Samaneh (Ed.): Interpretable and Annotation-Efficient Learning for Medical Image Computing, vol. 12446, pp. 204–213, Springer International Publishing, Cham, 2020, ISBN: 978-3-030-61165-1 978-3-030-61166-8, (Series Title: Lecture Notes in Computer Science).
@incollection{cardoso_are_2020,
title = {Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels},
author = {Christof A. Bertram and Mitko Veta and Christian Marzahl and Nikolas Stathonikos and Andreas Maier and Robert Klopfleisch and Marc Aubreville},
editor = {Jaime Cardoso and Hien Van Nguyen and Nicholas Heller and Pedro Henriques Abreu and Ivana Isgum and Wilson Silva and Ricardo Cruz and Jose Pereira Amorim and Vishal Patel and Badri Roysam and Kevin Zhou and Steve Jiang and Ngan Le and Khoa Luu and Raphael Sznitman and Veronika Cheplygina and Diana Mateus and Emanuele Trucco and Samaneh Abbasi},
url = {https://link.springer.com/10.1007/978-3-030-61166-8_22},
doi = {10.1007/978-3-030-61166-8_22},
isbn = {978-3-030-61165-1 978-3-030-61166-8},
year = {2020},
date = {2020-01-01},
urldate = {2023-06-30},
booktitle = {Interpretable and Annotation-Efficient Learning for Medical Image Computing},
volume = {12446},
pages = {204–213},
publisher = {Springer International Publishing},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Bertram, Christof A.; Jabari, Samir; Marzahl, Christian; Klopfleisch, Robert; Maier, Andreas
Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment: Learning New Tricks from Old Dogs Book Section
In: Tolxdorff, Thomas; Deserno, Thomas M.; Handels, Heinz; Maier, Andreas; Maier-Hein, Klaus H.; Palm, Christoph (Ed.): Bildverarbeitung für die Medizin 2020, pp. 1–7, Springer Fachmedien Wiesbaden, Wiesbaden, 2020, ISBN: 978-3-658-29266-9 978-3-658-29267-6, (Series Title: Informatik aktuell).
@incollection{tolxdorff_inter-species_2020,
title = {Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment: Learning New Tricks from Old Dogs},
author = {Marc Aubreville and Christof A. Bertram and Samir Jabari and Christian Marzahl and Robert Klopfleisch and Andreas Maier},
editor = {Thomas Tolxdorff and Thomas M. Deserno and Heinz Handels and Andreas Maier and Klaus H. Maier-Hein and Christoph Palm},
url = {http://link.springer.com/10.1007/978-3-658-29267-6_1},
doi = {10.1007/978-3-658-29267-6_1},
isbn = {978-3-658-29266-9 978-3-658-29267-6},
year = {2020},
date = {2020-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2020},
pages = {1–7},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Bertram, Christof A.; Aubreville, Marc; Gurtner, Corinna; Bartel, Alexander; Corner, S. M.; Dettwiler, M.; Kershaw, O.; Noland, E. L.; Schmidt, A.; Sledge, D. G.; Smedley, R. C.; Thaiwong, T.; Kiupel, Matti; Maier, A.; Klopfleisch, Robert
Mitotic Count in Canine Cutaneous Mast Cell Tumours – Not Accurate but Reproducible Journal Article
In: Journal of Comparative Pathology, vol. 174, pp. 143, 2020, ISSN: 00219975.
@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}
}
2019
Goncalves, Miguel; Aubreville, Marc; Mueller, Sarina K; Sievert, Matti; Maier, Andreas; Iro, Heinrich; Bohr, Christopher
Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds Journal Article
In: Acta Otorhinolaryngologica Italica, vol. 39, no. 6, pp. 389–395, 2019, ISSN: 1827-675X.
@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}
}
Bertram, Christof A.; Aubreville, Marc; Marzahl, Christian; Maier, Andreas; Klopfleisch, Robert
A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor Journal Article
In: Scientific Data, vol. 6, no. 1, pp. 274, 2019, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
Marzahl, Christian; Aubreville, Marc; Voigt, Jörn; Maier, Andreas
In: Gupta, Anubha; Gupta, Ritu (Ed.): ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging, pp. 13–22, Springer Singapore, Singapore, 2019, ISBN: 9789811507977 9789811507984, (Series Title: Lecture Notes in Bioengineering).
@incollection{gupta_classification_2019,
title = {Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques},
author = {Christian Marzahl and Marc Aubreville and Jörn Voigt and Andreas Maier},
editor = {Anubha Gupta and Ritu Gupta},
url = {http://link.springer.com/10.1007/978-981-15-0798-4_2},
doi = {10.1007/978-981-15-0798-4_2},
isbn = {9789811507977 9789811507984},
year = {2019},
date = {2019-01-01},
urldate = {2023-06-30},
booktitle = {ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging},
pages = {13–22},
publisher = {Springer Singapore},
address = {Singapore},
note = {Series Title: Lecture Notes in Bioengineering},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Stoeve, Maike; Oetter, Nicolai; Goncalves, Miguel; Knipfer, Christian; Neumann, Helmut; Bohr, Christopher; Stelzle, Florian; Maier, Andreas
Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images Journal Article
In: International Journal of Computer Assisted Radiology and Surgery, vol. 14, no. 1, pp. 31–42, 2019, ISSN: 1861-6410, 1861-6429.
@article{aubreville_deep_2019,
title = {Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images},
author = {Marc Aubreville and Maike Stoeve and Nicolai Oetter and Miguel Goncalves and Christian Knipfer and Helmut Neumann and Christopher Bohr and Florian Stelzle and Andreas Maier},
url = {http://link.springer.com/10.1007/s11548-018-1836-1},
doi = {10.1007/s11548-018-1836-1},
issn = {1861-6410, 1861-6429},
year = {2019},
date = {2019-01-01},
urldate = {2023-06-30},
journal = {International Journal of Computer Assisted Radiology and Surgery},
volume = {14},
number = {1},
pages = {31–42},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Aubreville, Marc; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas
Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks: Proceedings Article
In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 30–37, SCITEPRESS – Science and Technology Publications, Prague, Czech Republic, 2019, ISBN: 978-989-758-353-7.
@inproceedings{aubreville_field_2019,
title = {Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks:},
author = {Marc Aubreville and Christof Bertram and Robert Klopfleisch and Andreas Maier},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0007365700300037},
doi = {10.5220/0007365700300037},
isbn = {978-989-758-353-7},
year = {2019},
date = {2019-01-01},
urldate = {2025-02-11},
booktitle = {Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies},
pages = {30–37},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Prague, Czech Republic},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas
Augmented Mitotic Cell Count Using Field of Interest Proposal Book Section
In: Handels, Heinz; Deserno, Thomas M.; Maier, Andreas; Maier-Hein, Klaus Hermann; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2019, pp. 321–326, Springer Fachmedien Wiesbaden, Wiesbaden, 2019, ISBN: 978-3-658-25325-7 978-3-658-25326-4, (Series Title: Informatik aktuell).
@incollection{handels_augmented_2019,
title = {Augmented Mitotic Cell Count Using Field of Interest Proposal},
author = {Marc Aubreville and Christof A. Bertram and Robert Klopfleisch and Andreas Maier},
editor = {Heinz Handels and Thomas M. Deserno and Andreas Maier and Klaus Hermann Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-658-25326-4_71},
doi = {10.1007/978-3-658-25326-4_71},
isbn = {978-3-658-25325-7 978-3-658-25326-4},
year = {2019},
date = {2019-01-01},
urldate = {2025-02-11},
booktitle = {Bildverarbeitung für die Medizin 2019},
pages = {321–326},
publisher = {Springer Fachmedien Wiesbaden},
address = {Wiesbaden},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas
In: Cliquet, Alberto; Wiebe, Sheldon; Anderson, Paul; Saggio, Giovanni; Zwiggelaar, Reyer; Gamboa, Hugo; Fred, Ana; Badia, Sergi Bermúdez I (Ed.): Biomedical Engineering Systems and Technologies, vol. 1024, pp. 67–85, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-29195-2 978-3-030-29196-9, (Series Title: Communications in Computer and Information Science).
@incollection{cliquet_transferability_2019,
title = {Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract},
author = {Marc Aubreville and Miguel Goncalves and Christian Knipfer and Nicolai Oetter and Tobias Würfl and Helmut Neumann and Florian Stelzle and Christopher Bohr and Andreas Maier},
editor = {Alberto Cliquet and Sheldon Wiebe and Paul Anderson and Giovanni Saggio and Reyer Zwiggelaar and Hugo Gamboa and Ana Fred and Sergi Bermúdez I Badia},
url = {http://link.springer.com/10.1007/978-3-030-29196-9_4},
doi = {10.1007/978-3-030-29196-9_4},
isbn = {978-3-030-29195-2 978-3-030-29196-9},
year = {2019},
date = {2019-01-01},
urldate = {2025-02-11},
booktitle = {Biomedical Engineering Systems and Technologies},
volume = {1024},
pages = {67–85},
publisher = {Springer International Publishing},
address = {Cham},
note = {Series Title: Communications in Computer and Information Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
2018
Aubreville, Marc; Ehrensperger, Kai; Maier, Andreas; Rosenkranz, Tobias; Graf, Benjamin; Puder, Henning
Deep Denoising for Hearing Aid Applications Proceedings Article
In: 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 361–365, IEEE, Tokyo, 2018, ISBN: 978-1-5386-8151-0.
@inproceedings{aubreville_deep_2018,
title = {Deep Denoising for Hearing Aid Applications},
author = {Marc Aubreville and Kai Ehrensperger and Andreas Maier and Tobias Rosenkranz and Benjamin Graf and Henning Puder},
url = {https://ieeexplore.ieee.org/document/8521369/},
doi = {10.1109/IWAENC.2018.8521369},
isbn = {978-1-5386-8151-0},
year = {2018},
date = {2018-09-01},
urldate = {2023-06-30},
booktitle = {2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC)},
pages = {361–365},
publisher = {IEEE},
address = {Tokyo},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Steffes, Lara-Maria; Aubreville, Marc; Sesselmann, Stefan; Krenn, Veit; Maier, Andreas
Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slices Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus Hermann; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2018, pp. 228–233, Springer Berlin Heidelberg, Berlin, Heidelberg, 2018, ISBN: 978-3-662-56536-0 978-3-662-56537-7, (Series Title: Informatik aktuell).
@incollection{maier_classification_2018,
title = {Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slices},
author = {Lara-Maria Steffes and Marc Aubreville and Stefan Sesselmann and Veit Krenn and Andreas Maier},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Hermann Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-662-56537-7_63},
doi = {10.1007/978-3-662-56537-7_63},
isbn = {978-3-662-56536-0 978-3-662-56537-7},
year = {2018},
date = {2018-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2018},
pages = {228–233},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Mualla, Firas; Aubreville, Marc; Maier, Andreas
Microscopy Book Section
In: Maier, Andreas; Steidl, Stefan; Christlein, Vincent; Hornegger, Joachim (Ed.): Medical Imaging Systems, vol. 11111, pp. 69–90, Springer International Publishing, Cham, 2018, ISBN: 978-3-319-96519-2 978-3-319-96520-8, (Series Title: Lecture Notes in Computer Science).
@incollection{maier_microscopy_2018,
title = {Microscopy},
author = {Firas Mualla and Marc Aubreville and Andreas Maier},
editor = {Andreas Maier and Stefan Steidl and Vincent Christlein and Joachim Hornegger},
url = {http://link.springer.com/10.1007/978-3-319-96520-8_5},
doi = {10.1007/978-3-319-96520-8_5},
isbn = {978-3-319-96519-2 978-3-319-96520-8},
year = {2018},
date = {2018-01-01},
urldate = {2023-06-30},
booktitle = {Medical Imaging Systems},
volume = {11111},
pages = {69–90},
publisher = {Springer International Publishing},
address = {Cham},
note = {Series Title: Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Krappmann, Maximilian; Aubreville, Marc; Maier, Andereas; Bertram, Christof; Klopfleisch, Robert
Classification of Mitotic Cells: Potentials Beyond the Limits of Small Data Sets Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus Hermann; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2018, pp. 245–250, Springer Berlin Heidelberg, Berlin, Heidelberg, 2018, ISBN: 978-3-662-56536-0 978-3-662-56537-7, (Series Title: Informatik aktuell).
@incollection{maier_classification_2018-1,
title = {Classification of Mitotic Cells: Potentials Beyond the Limits of Small Data Sets},
author = {Maximilian Krappmann and Marc Aubreville and Andereas Maier and Christof Bertram and Robert Klopfleisch},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Hermann Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-662-56537-7_66},
doi = {10.1007/978-3-662-56537-7_66},
isbn = {978-3-662-56536-0 978-3-662-56537-7},
year = {2018},
date = {2018-01-01},
urldate = {2023-06-30},
booktitle = {Bildverarbeitung für die Medizin 2018},
pages = {245–250},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Stoeve, Maike; Aubreville, Marc; Oetter, Nicolai; Knipfer, Christian; Neumann, Helmut; Stelzle, Florian; Maier, Andreas
Motion Artifact Detection in Confocal Laser Endomicroscopy Images Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus Hermann; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2018, pp. 328–333, Springer Berlin Heidelberg, Berlin, Heidelberg, 2018, ISBN: 978-3-662-56536-0 978-3-662-56537-7, (Series Title: Informatik aktuell).
@incollection{maier_motion_2018,
title = {Motion Artifact Detection in Confocal Laser Endomicroscopy Images},
author = {Maike Stoeve and Marc Aubreville and Nicolai Oetter and Christian Knipfer and Helmut Neumann and Florian Stelzle and Andreas Maier},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Hermann Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-662-56537-7_85},
doi = {10.1007/978-3-662-56537-7_85},
isbn = {978-3-662-56536-0 978-3-662-56537-7},
year = {2018},
date = {2018-01-01},
urldate = {2025-02-11},
booktitle = {Bildverarbeitung für die Medizin 2018},
pages = {328–333},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas
SlideRunner: A Tool for Massive Cell Annotations in Whole Slide Images Book Section
In: Maier, Andreas; Deserno, Thomas M.; Handels, Heinz; Maier-Hein, Klaus Hermann; Palm, Christoph; Tolxdorff, Thomas (Ed.): Bildverarbeitung für die Medizin 2018, pp. 309–314, Springer Berlin Heidelberg, Berlin, Heidelberg, 2018, ISBN: 978-3-662-56536-0 978-3-662-56537-7, (Series Title: Informatik aktuell).
@incollection{maier_sliderunner_2018,
title = {SlideRunner: A Tool for Massive Cell Annotations in Whole Slide Images},
author = {Marc Aubreville and Christof Bertram and Robert Klopfleisch and Andreas Maier},
editor = {Andreas Maier and Thomas M. Deserno and Heinz Handels and Klaus Hermann Maier-Hein and Christoph Palm and Thomas Tolxdorff},
url = {http://link.springer.com/10.1007/978-3-662-56537-7_81},
doi = {10.1007/978-3-662-56537-7_81},
isbn = {978-3-662-56536-0 978-3-662-56537-7},
year = {2018},
date = {2018-01-01},
urldate = {2025-02-11},
booktitle = {Bildverarbeitung für die Medizin 2018},
pages = {309–314},
publisher = {Springer Berlin Heidelberg},
address = {Berlin, Heidelberg},
note = {Series Title: Informatik aktuell},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas
Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images – A Cross-site Robustness Assessment: Proceedings Article
In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 27–34, SCITEPRESS – Science and Technology Publications, Funchal, Madeira, Portugal, 2018, ISBN: 978-989-758-307-0.
@inproceedings{aubreville_patch-based_2018,
title = {Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment:},
author = {Marc Aubreville and Miguel Goncalves and Christian Knipfer and Nicolai Oetter and Tobias Würfl and Helmut Neumann and Florian Stelzle and Christopher Bohr and Andreas Maier},
url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006534700270034},
doi = {10.5220/0006534700270034},
isbn = {978-989-758-307-0},
year = {2018},
date = {2018-01-01},
urldate = {2025-02-11},
booktitle = {Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies},
pages = {27–34},
publisher = {SCITEPRESS - Science and Technology Publications},
address = {Funchal, Madeira, Portugal},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
Aubreville, Marc; Knipfer, Christian; Oetter, Nicolai; Jaremenko, Christian; Rodner, Erik; Denzler, Joachim; Bohr, Christopher; Neumann, Helmut; Stelzle, Florian; Maier, Andreas
Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning Journal Article
In: Scientific Reports, vol. 7, no. 1, pp. 11979, 2017, ISSN: 2045-2322.
Abstract | Links | BibTeX | Tags:
@article{aubreville_automatic_2017,
title = {Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning},
author = {Marc Aubreville and Christian Knipfer and Nicolai Oetter and Christian Jaremenko and Erik Rodner and Joachim Denzler and Christopher Bohr and Helmut Neumann and Florian Stelzle and Andreas Maier},
url = {https://www.nature.com/articles/s41598-017-12320-8},
doi = {10.1038/s41598-017-12320-8},
issn = {2045-2322},
year = {2017},
date = {2017-09-01},
urldate = {2023-06-30},
journal = {Scientific Reports},
volume = {7},
number = {1},
pages = {11979},
abstract = {Abstract
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for
in vivo
cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC
in situ
. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for
in vivo
cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC
in situ
. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).
Aubreville, Marc; Krappmann, Maximilian; Bertram, Christof; Klopfleisch, Robert; Maier, Andreas
A Guided Spatial Transformer Network for Histology Cell Differentiation Miscellaneous
2017, (Artwork Size: 5 pages ISBN: 9783038680369 ISSN: 2070-5786 Pages: 5 pages Publication Title: Eurographics Workshop on Visual Computing for Biology and Medicine).
Abstract | Links | BibTeX | Tags: Applied computing, Bioinformatics, Computing methodologies, FOS: Computer and information sciences, Neural networks, Object detection
@misc{aubreville_guided_2017,
title = {A Guided Spatial Transformer Network for Histology Cell Differentiation},
author = {Marc Aubreville and Maximilian Krappmann and Christof Bertram and Robert Klopfleisch and Andreas Maier},
url = {https://diglib.eg.org/handle/10.2312/vcbm20171233},
doi = {10.2312/VCBM.20171233},
year = {2017},
date = {2017-01-01},
urldate = {2025-02-11},
publisher = {The Eurographics Association},
abstract = {Identification and counting of cells and mitotic figures is a standard task in diagnostic histopathology. Due to the large overall cell count on histological slides and the potential sparse prevalence of some relevant cell types or mitotic figures, retrieving annotation data for sufficient statistics is a tedious task and prone to a significant error in assessment. Automatic classification and segmentation is a classic task in digital pathology, yet it is not solved to a sufficient degree. We present a novel approach for cell and mitotic figure classification, based on a deep convolutional network with an incorporated Spatial Transformer Network. The network was trained on a novel data set with ten thousand mitotic figures, about ten times more than previous data sets. The algorithm is able to derive the cell class (mitotic tumor cells, non-mitotic tumor cells and granulocytes) and their position within an image. The mean accuracy of the algorithm in a five-fold cross-validation is 91.45 %. In our view, the approach is a promising step into the direction of a more objective and accurate, semi-automatized mitosis counting supporting the pathologist.},
note = {Artwork Size: 5 pages
ISBN: 9783038680369
ISSN: 2070-5786
Pages: 5 pages
Publication Title: Eurographics Workshop on Visual Computing for Biology and Medicine},
keywords = {Applied computing, Bioinformatics, Computing methodologies, FOS: Computer and information sciences, Neural networks, Object detection},
pubstate = {published},
tppubtype = {misc}
}