NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.

ABSTRACT: The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segme

SUBMITTER: Wu L 

PROVIDER: S-EPMC10261124 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature