Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation.

ABSTRACT: Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustwo

SUBMITTER: Guo A 

PROVIDER: S-EPMC10087011 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature