Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets.

ABSTRACT: Segmenting three-dimensional (3D) microscopy images is essential for understanding phenomena like morphogenesis, cell division, cellular growth, and genetic expression patterns. Recently, deep learning (DL) pipelines have been developed, which claim to provide high accuracy segmentation of cellular images and are increasingly considered as the state of the art for image segmentation problems. However, it remains difficult to define their relative performances as the concurrent diversity and lack of uniform evaluation strategies makes it difficult to know how their results compare. In this paper, we first made an inventory of the available DL methods for 3D cell segmentation. We next implemented and quantitatively compared a number of representative DL pipelines, alongside a highly efficien

SUBMITTER: Kar A 

PROVIDER: S-EPMC9009699 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature