Slim: interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

TL;DR

Slim is an open-source, web-based slide microscopy viewer that uses the DICOM standard to improve data interoperability, visualization, and annotation in biomedical imaging and pathology research.

Contribution

It introduces Slim, a novel interoperable slide microscopy viewer that supports standard DICOMweb services for enhanced data sharing and analysis.

Findings

Successfully integrated with NCI Imaging Data Commons.

Enabled visualization of diverse microscopy images.

Supported standardized image annotations for machine learning.

Abstract

The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. Slim is an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.