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A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor

  • 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 mitoticWe 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.show moreshow less

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Metadaten
Author:Christof BertramORCiD, Marc AubrevilleORCiD, Christian MarzahlORCiD, Andreas MaierORCiD, Robert KlopfleischORCiD
Language:English
Document Type:Article
Year of first Publication:2019
published in (English):Scientific data
Publisher:Nature
Place of publication:London
ISSN:2052-4463
Volume:6
Pages:9
Article Number:274
Review:peer-review
Open Access:ja
Version:published
URN:urn:nbn:de:bvb:573-11838
Related Identifier:https://doi.org/10.1038/s41597-019-0290-4
Licence (German):License Logo Creative Commons BY 4.0
Release Date:2022/02/10