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Virtual alignment of pathology image series for multi-gigapixel whole slide images

Author

Listed:
  • Chandler D. Gatenbee

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Ann-Marie Baker

    (Queen Mary University of London)

  • Sandhya Prabhakaran

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Ottilie Swinyard

    (Queen Mary University of London)

  • Robbert J. C. Slebos

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Gunjan Mandal

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Eoghan Mulholland

    (University of Oxford)

  • Noemi Andor

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Andriy Marusyk

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Simon Leedham

    (University of Oxford)

  • Jose R. Conejo-Garcia

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Christine H. Chung

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Mark Robertson-Tessi

    (H. Lee Moffitt Cancer Center & Research Institute)

  • Trevor A. Graham

    (Queen Mary University of London)

  • Alexander R. A. Anderson

    (H. Lee Moffitt Cancer Center & Research Institute)

Abstract

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

Suggested Citation

  • Chandler D. Gatenbee & Ann-Marie Baker & Sandhya Prabhakaran & Ottilie Swinyard & Robbert J. C. Slebos & Gunjan Mandal & Eoghan Mulholland & Noemi Andor & Andriy Marusyk & Simon Leedham & Jose R. Cone, 2023. "Virtual alignment of pathology image series for multi-gigapixel whole slide images," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40218-9
    DOI: 10.1038/s41467-023-40218-9
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    References listed on IDEAS

    as
    1. Jun Jiang & Nicholas B Larson & Naresh Prodduturi & Thomas J Flotte & Steven N Hart, 2019. "Robust hierarchical density estimation and regression for re-stained histological whole slide image co-registration," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-11, July.
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