Sharing standardized image-derived data in computational pathology using DICOM

Authors:Daniela P. Schacherer (1), Christopher P. Bridge (2, 3), David Clunie (4), Igor Octaviano (5), André Homeyer (1), Markus D. Herrmann (3, 6), Olivier Gevaert (7), Tabita Ghete (8), Markus Metzler (8), Henning Hoefener (1), Tahsin Kurc (9), Curtis Lisle (10), Kenneth Philbrick (11), Joel Saltz (9), Yuanning Zheng (7), Andrey Fedorov (3, 12) ((1) Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, (2) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA, (3) Harvard Medical School, Boston, USA, (4) PixelMed Publishing, Bangor, USA, (5) Radical Imaging LLC, USA, (6) Massachusetts General Hospital, Boston, USA, (7) Stanford University, Stanford, USA, (8) University Hospital Erlangen, Erlangen, Germany, (9) Stony Brook University, Stony Brook, USA, (10) National Institute of Allergy and Infectious Diseases, Bethesda, USA, (11) Google Research, Mountain View, USA, (12) Brigham and Women's Hospital, Boston, USA)

Abstract:Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into collecting, centralizing, and sharing pathology imaging data. In contrast, sharing of image-derived data such as region-of-interest delineations or segmentation masks is less well developed. In this work, we describe our approach to encoding and sharing image-derived pathology data in a standardized manner within the National Cancer Institute (NCI) Imaging Data Commons (IDC), a platform that hosts and provides public access to de-identified radiology and pathology data. The IDC relies on the Digital Imaging and Communications in Medicine (DICOM) standard for data harmonization, yet the adoption of DICOM for pathology image-derived content has remained largely unexplored until now. Here, we present five representative datasets harmonized by conversion from their original representations into DICOM and shared publicly in the IDC. We demonstrate the benefits of this harmonization, describe contributions to critical open-source tooling, and discuss technical considerations relevant to broader adoption of DICOM for pathology image-derived data.

Daniela P. Schacherer, Christopher P. Bridge: contributed equally

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.14530 [cs.CV]

(or arXiv:2609.14530v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.14530 arXiv-issued DOI via DataCite (pending registration)