ArticleJournal of pathology informatics2026
An automated end-to-end pipeline for the management, de-identification, and distribution of whole-slide images using DICOM: An institutional implementation.
Article in Journal of pathology informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Medical imaging data are an essential resource for research and teaching; however, regulations such as the Health Insurance Portability and Accountability Act require careful consideration of protected health information (PHI) contained within images and their metadata. De-identification, the removal of PHI to prevent subject re-identification, is often essential for regulatory compliance. In pathology, whole-slide imaging (WSI) files-high-resolution scans of entire glass slides-are essential for research and machine learning model training. Currently, many scanner vendors use proprietary WSI formats; some vendors offer de-identification tools, which are often manual and tedious to use. Alternatively, physical slides may be re-scanned with obfuscated labels, though this is resource intensive. Here, we describe an institutional-scale automated WSI de-identification pipeline, which utilizes an informatics-based approach to convert clinical WSI from 11 proprietary formats into de-identified WSI in multiple open formats. This approach minimizes manual processes and additional slide-scanning hardware, storage, and personnel. De-identification requests are submitted through a zero-footprint web portal following the Fast Healthcare Interoperability Resources ServiceRequest data model. De-identification is verified via a human-in-the-loop review, and archives are distributed using cloud-based storage. Since deployment in November 2024, our pipeline has de-identified 819 cases comprising 4322 WSI files generated by 7 scanner models in 4 formats. We demonstrate that the conversion process is predictable, linearly scalable, and reliable across de-identification request sizes (238× variation), image sizes (120× variation), and capture format. This pipeline eliminates the need for manual de-identification or re-scanning while achieving high throughput and reliability at an institutional scale.
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