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

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. The spatial organization of a tumor affects how it grows and responds to treatment. Here, the authors present VALIS, a software to align sets of whole slide images (WSI) with state-of-the-art accuracy, enabling spatial studies of the tumor ecology.

Automated summary

Interest in spatial omics is growing, but generating highly multiplexed images is challenging due to various factors. A new software called VALIS has been developed to address these challenges by aligning whole slide images (WSI) and generating high-quality, spatially aligned datasets. VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction, making it a valuable tool for spatial studies of tumor ecology.