Pseudo-spectral angle mapping for pixel and cell classification in highly multiplexed immunofluorescence images.

Autor:	Torcasso MS; The University of Chicago, Department of Radiology, Chicago, Illinois, United States.; The University of Chicago, Department of Medicine, Section on Rheumatology, Chicago, Illinois, United States., Ai J; The University of Chicago, Department of Medicine, Section on Rheumatology, Chicago, Illinois, United States., Casella G; The University of Chicago, Department of Radiology, Chicago, Illinois, United States.; The University of Chicago, Department of Medicine, Section on Rheumatology, Chicago, Illinois, United States., Cao T; The University of Chicago, Pritzker School of Molecular Engineering, Chicago, Illinois, United States., Chang A; The University of Chicago, Department of Pathology, Chicago, Illinois, United States., Halper-Stromberg A; The University of Chicago, Department of Medicine, Section on Gastroenterology, Hepatology and Nutrition, Chicago, Illinois, United States., Jabri B; The University of Chicago, Department of Medicine, Section on Gastroenterology, Hepatology and Nutrition, Chicago, Illinois, United States., Clark MR; The University of Chicago, Department of Medicine, Section on Rheumatology, Chicago, Illinois, United States., Giger ML; The University of Chicago, Department of Radiology, Chicago, Illinois, United States.
Jazyk:	angličtina
Zdroj:	Journal of medical imaging (Bellingham, Wash.) [J Med Imaging (Bellingham)] 2024 Nov; Vol. 11 (6), pp. 067502. Date of Electronic Publication: 2024 Dec 10.
DOI:	10.1117/1.JMI.11.6.067502
Abstrakt:	Purpose: The rapid development of highly multiplexed microscopy has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging; there is a need for more robust and generalizable tools for evaluating the cellular constituents and stroma captured by high-plex imaging. To address this need, we have adapted spectral angle mapping-an algorithm developed for hyperspectral image analysis-to compress the channel dimension of high-plex immunofluorescence (IF) images. Approach: Here, we present pseudo-spectral angle mapping (pSAM), a robust and flexible method for determining the most likely class of each pixel in a high-plex image. The class maps calculated through pSAM yield pixel classifications which can be combined with instance segmentation algorithms to classify individual cells. Results: In a dataset of colon biopsies imaged with a 13-plex staining panel, 16 pSAM class maps were computed to generate pixel classifications. Instance segmentations of cells with Cellpose2.0 ( F 1 -score of 0.83 ± 0.13 ) were combined with these class maps to provide cell class predictions for 13 cell classes. In addition, in a separate unseen dataset of kidney biopsies imaged with a 44-plex staining panel, pSAM plus Cellpose2.0 ( F 1 -score of 0.86 ± 0.11 ) detected a diverse set of 38 classes of structural and immune cells. Conclusions: In summary, pSAM is a powerful and generalizable tool for evaluating high-plex IF image data and classifying cells in these high-dimensional images. (© 2024 The Authors.)
Databáze:	MEDLINE
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