DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data.

ABSTRACT: The scale of biological microscopy has increased dramatically over the past ten years, with the development of new modalities supporting collection of high-resolution fluorescence image volumes spanning hundreds of microns if not millimeters. The size and complexity of these volumes is such that quantitative analysis requires automated methods of image processing to identify and characterize individual cells. For many workflows, this process starts with segmentation of nuclei that, due to their ubiquity, ease-of-labeling and relatively simple structure, make them appealing targets for automated detection of individual cells. However, in the context of large, three-dimensional image volumes, nuclei present many challenges to automated segmentation, such that conventional approaches are seld

SUBMITTER: Dunn KW 

PROVIDER: S-EPMC6892824 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature