Nuclei Detection for 3D Microscopy With a Fully Convolutional Regression Network.

ABSTRACT: Advances in three-dimensional microscopy and tissue clearing are enabling whole-organ imaging with single-cell resolution. Fast and reliable image processing tools are needed to analyze the resulting image volumes, including automated cell detection, cell counting and cell analytics. Deep learning approaches have shown promising results in two- and three-dimensional nuclei detection tasks, however detecting overlapping or non-spherical nuclei of different sizes and shapes in the presence of a blurring point spread function remains challenging and often leads to incorrect nuclei merging and splitting. Here we present a new regression-based fully convolutional network that located a thousand nuclei centroids with high accuracy in under a minute when combined with V-net, a popular three-dimen

SUBMITTER: Lapierre-Landry M 

PROVIDER: S-EPMC8751907 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature