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 - JOUR A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Donovan, Taryn A1 - Marzahl, Christian A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research JF - Scientific data N2 - 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. UR - https://doi.org/10.1038/s41597-020-00756-z Y1 - 2020 UR - https://doi.org/10.1038/s41597-020-00756-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11765 SN - 2052-4463 VL - 7 PB - Springer CY - London ER - TY - JOUR A1 - Marzahl, Christian A1 - Hill, Jenny A1 - Stayt, Jason A1 - Bienzle, Dorothee A1 - Welker, Lutz A1 - Wilm, Frauke A1 - Voigt, Jörn A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Breininger, Katharina A1 - Bertram, Christof T1 - Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens JF - Scientific Data N2 - Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered. UR - https://doi.org/10.1038/s41597-022-01389-0 Y1 - 2022 UR - https://doi.org/10.1038/s41597-022-01389-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29319 SN - 2052-4463 VL - 9 PB - Springer CY - London ER - TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Marzahl, Christian A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor JF - Scientific data N2 - 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. UR - https://doi.org/10.1038/s41597-019-0290-4 Y1 - 2019 UR - https://doi.org/10.1038/s41597-019-0290-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11838 SN - 2052-4463 VL - 6 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 - TY - INPR A1 - Bertram, Christof A1 - Veta, Mitko A1 - Marzahl, Christian A1 - Stathonikos, Nikolas A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels UR - https://doi.org/10.48550/arXiv.2007.05351 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2007.05351 PB - arXiv CY - Ithaca ER -