CellPilot: A Unified Approach to Automatic and Interactive Segmentation in Histopathology

TY - CHAP

T1 - CellPilot

T2 - 22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

AU - Endres, Philipp

AU - Koch, Valentin

AU - Schnabel, Julia A.

AU - Marr, Carsten

N1 - Publisher Copyright: © 2025 IEEE.

PY - 2025/5/12

Y1 - 2025/5/12

N2 - 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.

AB - 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.

KW - cell segmentation

KW - gland segmentation

KW - medical imaging

UR - http://www.scopus.com/inward/record.url?scp=105005834112&partnerID=8YFLogxK

U2 - 10.1109/ISBI60581.2025.10980782

DO - 10.1109/ISBI60581.2025.10980782

M3 - Conference paper

AN - SCOPUS:105005834112

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - ISBI 2025 - 2025 IEEE 22nd International Symposium on Biomedical Imaging, Proceedings

PB - IEEE Computer Society

Y2 - 14 April 2025 through 17 April 2025

ER -