A comprehensive multi-domain dataset for mitotic figure detection

Aubreville M, Wilm F, Stathonikos N, Breininger K, Donovan TA, Jabari S, Veta M, Ganz J, Ammeling J, van Diest PJ, Klopfleisch R, Bertram CA (2023)

Publication Type: Journal article

Publication year: 2023

Journal

Scientific Data Nature Publishing Group

Book Volume: 10

Article Number: 484

Journal Issue: 1

DOI: 10.1038/s41597-023-02327-4

Abstract

The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.

Authors with CRIS profile

Frauke Wilm Lehrstuhl für Informatik 5 (Mustererkennung) Katharina Breininger Juniorprofessur für Artificial Intelligence in Medical Imaging Samir Jabari Lehrstuhl für Mikroskopische Anatomie und Molekulare Zellbiologie

Involved external institutions

Technische Hochschule Ingolstadt DE Germany (DE) Eindhoven University of Technology / Technische Universiteit Eindhoven (TU/e) NL Netherlands (NL) University Medical Centre Utrecht (UMC Utrecht) NL Netherlands (NL) Animal Medical Center (AMC) US United States (USA) (US) Veterinärmedizinische Universität Wien (Vetmeduni Vienna) / University of Veterinary Medicine, Vienna AT Austria (AT) Freie Universität Berlin DE Germany (DE)

How to cite

APA:

Aubreville, M., Wilm, F., Stathonikos, N., Breininger, K., Donovan, T.A., Jabari, S.,... Bertram, C.A. (2023). A comprehensive multi-domain dataset for mitotic figure detection. Scientific Data, 10(1). https://doi.org/10.1038/s41597-023-02327-4

MLA:

Aubreville, Marc, et al. "A comprehensive multi-domain dataset for mitotic figure detection." Scientific Data 10.1 (2023).

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