Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens

Language
en
Document Type
Article
Issue Date
2023-07-04
Issue Year
2022
Authors
Marzahl, Christian
Hill, Jenny
Stayt, Jason
Bienzle, Dorothee
Welker, Lutz
Wilm, Frauke
Voigt, Jörn
Aubreville, Marc
Maier, Andreas
Klopfleisch, Robert
Editor
Publisher
Springer Nature
Abstract

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.

Journal Title
Scientific Data
Volume
9
Citation
Scientific Data 9 (2022): 269. <https://www.nature.com/articles/s41597-022-01389-0>
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