Sharing standardized image-derived data in computational pathology using DICOM

2026-09-13

AI Summary

本文解决了病理图像衍生数据共享不足的问题，通过将这些数据转换为DICOM标准格式并在NCI IDC平台上公开分享来解决。

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.

Problem

• computational pathology
• image-derived data
• DICOM
• data sharing
• standardization

Innovation

• DICOM
• image-derived data
• computational pathology
• standardization
• data harmonization