Publikationsdatenbank

A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor (2019)

Art

Zeitschriftenartikel / wissenschaftlicher Beitrag

Autoren

Bertram, Christof A. (WE 12)

Aubreville, Marc

Marzahl, Christian

Maier, Andreas

Klopfleisch, Robert (WE 12)

Quelle

Scientific data

Bandzählung: 6

Heftzählung: 1

Seiten: Article number: 274

ISSN: 2052-4463

Sprache

Englisch

Verweise

URL (Volltext): https://www.nature.com/articles/s41597-019-0290-4.pdf

DOI: 10.1038/s41597-019-0290-4

Pubmed: 31754105

Kontakt

Institut für Tierpathologie

Robert-von-Ostertag-Str. 15
14163 Berlin
+49 30 838 62450
pathologie@vetmed.fu-berlin.de

Abstract / Zusammenfassung

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.

Measurement(s): Mitotic Figure
Technology Type(s): visual observation method • machine learning
Factor Type(s): tumor grade • experimental method
Sample Characteristic - Organism: Canis

Machine-accessible metadata file describing the reported data:
https://doi.org/10.6084/m9.figshare.9952469