Sharing standardized image-derived data in computational pathology using DICOM
Published 16 Sept 2026arXiv:2609.14530
Updated 7 h ago · first seen 15 Sept 2026
paper_01M2JK0CN34RW41V65ZTQSGMHC
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.
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Sharing standardized image-derived data in computational pathology using DICOM: arxiv announce type changed from new to cross
Arxiv announce typenew→crossarxivSharing standardized image-derived data in computational pathology using DICOM: published at changed from 2026-09-15T04:00:00+00:00 to 2026-09-16T04:00:00+00:00
Published15 Sept 2026→16 Sept 2026arxivSharing standardized image-derived data in computational pathology using DICOM: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxivNew paper: Sharing standardized image-derived data in computational pathology using DICOM
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