Automated cell boundary and 3D nuclear segmentation of cells in suspension.

ABSTRACT: To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data ac

SUBMITTER: Kesler B 

PROVIDER: S-EPMC6629630 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature