ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.

ABSTRACT: Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treatment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here, we introduce the machine-learning pipeline "ImmuNet", which identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker-drawing a full cell outli

SUBMITTER: Sultan S 

PROVIDER: S-EPMC11769680 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature