TY - CHAP A1 - Bertram, Christof A1 - Donovan, Taryn A1 - Tecilla, Marco A1 - Bartenschlager, Florian A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Dataset on bi- and multi-nucleated tumor cells in canine cutaneous mast cell tumors T2 - Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7–9, 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_33 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_33 SN - 978-3-658-33197-9 SN - 978-3-658-33198-6 SN - 1431-472X SP - 134 EP - 139 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Stayt, Jason A1 - Jasensky, Anne-Katherine A1 - Bartenschlager, Florian A1 - Fragoso-Garcia, Marco A1 - Barton, Ann K. A1 - Elsemann, Svenja A1 - Jabari, Samir A1 - Krauth, Jens A1 - Madhu, Prathmesh A1 - Voigt, Jörn A1 - Hill, Jenny A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Deep Learning-based quantification of pulmonary hemosiderophages in cytology slides JF - Scientific Reports N2 - 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. UR - https://doi.org/10.1038/s41598-020-65958-2 Y1 - 2020 UR - https://doi.org/10.1038/s41598-020-65958-2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11783 SN - 2045-2322 VL - 10 PB - Springer Nature CY - London ER - TY - JOUR A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Gurtner, Corinne A1 - Dettwiler, Martina A1 - Schmidt, Anja A1 - Bartenschlager, Florian A1 - Merz, Sophie A1 - Fragoso-Garcia, Marco A1 - Kershaw, Olivia A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region JF - Scientific reports N2 - 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. UR - https://doi.org/10.1038/s41598-020-73246-2 Y1 - 2020 UR - https://doi.org/10.1038/s41598-020-73246-2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11794 SN - 2045-2322 VL - 10 PB - Springer Nature CY - London ER - TY - INPR A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - Öttl, Mathias A1 - Qiu, Jingna A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology UR - https://doi.org/10.48550/arXiv.2211.16141 KW - Histopathology KW - Domain Shift KW - Representation Learning KW - Barlow Twins Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2211.16141 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Petrick, Anne A1 - Weiler, Kristina A1 - Gläsel, Agnes C. A1 - Fragoso-Garcia, Marco A1 - Merz, Sophie A1 - Bartenschlager, Florian A1 - Hoppe, Judith A1 - Langenhagen, Alina A1 - Jasensky, Anne-Katherine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 UR - https://doi.org/10.1007/978-3-030-59710-8_3 KW - Pathology KW - Microscopy KW - Computer-aided labelling KW - Expert-algorithm collaboration Y1 - 2020 UR - https://doi.org/10.1007/978-3-030-59710-8_3 SN - 978-3-030-59710-8 SN - 1611-3349 SP - 24 EP - 32 PB - Springer CY - Cham ER - TY - CHAP A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - Öttl, Mathias A1 - Qiu, Jingna A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology T2 - 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI53787.2023.10230458 KW - Histopathology KW - Domain Shift KW - Representation Learning KW - Barlow Twins Y1 - 2023 UR - https://doi.org/10.1109/ISBI53787.2023.10230458 SN - 978-1-6654-7358-3 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Merz, Sophie A1 - Schmidt, Anja A1 - Donovan, Taryn A1 - Fuchs-Baumgartinger, Andrea A1 - Bartel, Alexander A1 - Marzahl, Christian A1 - Diehl, Laura A1 - Puget, Chloe A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina A1 - Klopfleisch, Robert T1 - Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images JF - Veterinary Pathology N2 - 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, n = 245 whole-slide images (WSIs), validation ( n = 35 WSIs), and test sets ( n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types. UR - https://doi.org/10.1177/03009858231189205 KW - computer-aided diagnosis KW - computational pathology KW - digital pathology KW - dog KW - machine learning KW - skin KW - veterinary oncology Y1 - 2023 UR - https://doi.org/10.1177/03009858231189205 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38321 SN - 0300-9858 VL - 60 IS - 6 SP - 865 EP - 875 PB - Sage CY - London ER - TY - JOUR A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Marzahl, Christian A1 - Qiu, Jingna A1 - Puget, Chloé A1 - Diehl, Laura A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset JF - Scientific Data N2 - 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. UR - https://doi.org/10.1038/s41597-022-01692-w Y1 - 2022 UR - https://doi.org/10.1038/s41597-022-01692-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28741 SN - 2052-4463 VL - 9 PB - Springer CY - London ER - TY - INPR A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Petrick, Anne A1 - Weiler, Kristina A1 - Gläsel, Agnes C. A1 - Fragoso-Garcia, Marco A1 - Merz, Sophie A1 - Bartenschlager, Florian A1 - Hoppe, Judith A1 - Langenhagen, Alina A1 - Jasensky, Anne-Katherine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides UR - https://doi.org/10.48550/arXiv.2004.05838 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2004.05838 PB - arXiv CY - Ithaca ER -