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

Institution: Lee Moffitt Cancer Center and Research Institute

Corresponding Researcher: Alexander Anderson

Email: Alexander.Anderson@moffitt.org

Publication Link(s): https://doi.org/10.1038/s41467-023-40218-9

Data Link(s): The ANHIR Grand Challenge dataset is available at https://anhir.grand-challenge.org/Data/. The ACROBAT Grand Challenge datasets are available at https://acrobat.grand-challenge.org/. The 3D datasets are available at http://urn.fi/urn:nbn:fi:csc-kata20170705131652639702. All other images are part of on-going studies and will be made available upon their publication.

Keyword(s): spatial omics, whole slide images, virtual alignment

Summary

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.