Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images.

ABSTRACT: Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network

SUBMITTER: Nandy K 

PROVIDER: S-EPMC6362837 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature