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

ABSTRACT: Multiplexed imaging technologies have made it possible to interrogate complex tissue 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 unsupervised context. Here we present an easy-to-use unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data via leveraging a Bayesian-like framework, and nucleus and cell membrane markers. We show t

SUBMITTER: Kochetov B 

PROVIDER: S-EPMC11364851 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature