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

Author

Listed:
  • Chris Gorman

    (Massachusetts General Hospital and Harvard Medical School)

  • Davide Punzo

    (Radical Imaging)

  • Igor Octaviano

    (Radical Imaging)

  • Steven Pieper

    (Isomics Inc)

  • William J. R. Longabaugh

    (Institute for Systems Biology)

  • David A. Clunie

    (PixelMed Publishing LLC)

  • Ron Kikinis

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Andrey Y. Fedorov

    (Brigham and Women’s Hospital and Harvard Medical School)

  • Markus D. Herrmann

    (Massachusetts General Hospital and Harvard Medical School)

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. 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.

Suggested Citation

  • Chris Gorman & Davide Punzo & Igor Octaviano & Steven Pieper & William J. R. Longabaugh & David A. Clunie & Ron Kikinis & Andrey Y. Fedorov & Markus D. Herrmann, 2023. "Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37224-2
    DOI: 10.1038/s41467-023-37224-2
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    References listed on IDEAS

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    1. Fabio Crameri & Grace E. Shephard & Philip J. Heron, 2020. "The misuse of colour in science communication," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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