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Endres, P. ; Koch, V. ; Schnabel, J.* ; Marr, C.

CellPilot: A unified approach to automatic and interactive segmentation in histopathology.

In: (22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025, 14-17 April 2025, Houston). 2025. (Proceedings - International Symposium on Biomedical Imaging)
DOI
Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learning tools are widely used in histopathology. However, variability in tissue appearance and cell morphology presents challenges for achieving reliable segmentation, often requiring manual correction to improve accuracy. This work introduces CellPilot, a framework that bridges the gap between automatic and interactive segmentation by providing initial automatic segmentation as well as guided interactive refinement. Our model was trained on over 675,000 masks of nine diverse cell and gland segmentation datasets, spanning 16 organs. CellPilot demonstrates superior performance compared to other interactive tools on three held-out histopathological datasets while enabling automatic segmentation. We make the model and a graphical user interface designed to assist practitioners in creating large-scale annotated datasets available as open-source, fostering the development of more robust and generalized diagnostic models. 11https://github.com/philippendres/CellPilot.
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Publication type Article: Conference contribution
Corresponding Author
Keywords Cell Segmentation ; Gland Segmentation ; Medical Imaging
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Conference Title 22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025
Conference Date 14-17 April 2025
Conference Location Houston
Non-patent literature Publications
Institute(s) Institute of AI for Health (AIH)