Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology

• Published in: Nature communications Vol. 14; no. 1; p. 1572
• Main Authors: Gorman, Chris, Punzo, Davide, Octaviano, Igor, Pieper, Steven, Longabaugh, William J. R., Clunie, David A., Kikinis, Ron, Fedorov, Andrey Y., Herrmann, Markus D.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.03.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2401; 631/114/2416; 631/114/794; 692/700/1421; Biomedical data; Computer applications; Data Science; Digital imaging; Gene mapping; Genomes; Human tissues; Humanities and Social Sciences; Humans; Image annotation; Image processing; Image segmentation; Immunofluorescence; Interoperability; Machine learning; Medical imaging; Medical research; Microscopy; Microscopy - methods; multidisciplinary; Pathology; Reproducibility of Results; Science; Science (multidisciplinary); Standardization; Technological change; Visual discrimination learning
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-37224-2

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. We introduce Slim, 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. There is a lack of standardisation in slide microscopy imaging data. Here the authors report Slim, an open-source, web-based slide microscopy viewer implementing the Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a range of existing medical imaging systems.