UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samples.

ABSTRACT: Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane

SUBMITTER: Kochetov B 

PROVIDER: S-EPMC10680584 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature