Virtual alignment of pathology image series for multi-gigapixel whole slide images.
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ICR Authors
Authors
Gatenbee, CD
Baker, A-M
Prabhakaran, S
Swinyard, O
Slebos, RJC
Mandal, G
Mulholland, E
Andor, N
Marusyk, A
Leedham, S
Conejo-Garcia, JR
Chung, CH
Robertson-Tessi, M
Graham, TA
Anderson, ARA
Baker, A-M
Prabhakaran, S
Swinyard, O
Slebos, RJC
Mandal, G
Mulholland, E
Andor, N
Marusyk, A
Leedham, S
Conejo-Garcia, JR
Chung, CH
Robertson-Tessi, M
Graham, TA
Anderson, ARA
Document Type
Journal Article
Date
2023-07-26
Date Accepted
2023-07-13
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.
Citation
Nature Communications, 2023, 14 (1), pp. 4502 -
Source Title
Nature Communications
Publisher
NATURE PORTFOLIO
ISSN
2041-1723
eISSN
2041-1723
2041-1723
2041-1723
Collections
Research Team
Genomics & evolut dynam