Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.

ABSTRACT:

Background

Nuclear segmentation is an important step for profiling aberrant regions of histology sections. If nuclear segmentation can be resolved, then new biomarkers of nuclear phenotypes and their organization can be predicted for the application of precision medicine. However, segmentation is a complex problem as a result of variations in nuclear geometry (e.g., size, shape), nuclear type (e.g., epithelial, fibroblast), nuclear phenotypes (e.g., vesicular, aneuploidy), and overlapping nuclei. The problem is further complicated as a result of variations in sample preparation (e.g., fixation, staining). Our hypothesis is that (i) deep learning techniques can learn complex phenotypic signatures that rise in tumor sections, and (ii) fusion of different representations (e.g., region

SUBMITTER: Khoshdeli M 

PROVIDER: S-EPMC6081825 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature