Segmentation of Image Data from Complex Organotypic 3D Models of Cancer Tissues with Markov Random Fields.

ABSTRACT: Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co

SUBMITTER: Robinson S 

PROVIDER: S-EPMC4668034 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature