A deep learning-based toolkit for 3D nuclei segmentation and quantitative analysis in cellular and tissue context.

ABSTRACT: We present a new set of computational tools that enable accurate and widely applicable 3D segmentation of nuclei in various 3D digital organs. We have developed an approach for ground truth generation and iterative training of 3D nuclear segmentation models, which we applied to popular CellPose, PlantSeg and StarDist algorithms. We provide two high-quality models trained on plant nuclei that enable 3D segmentation of nuclei in datasets obtained from fixed or live samples, acquired from different plant and animal tissues, and stained with various nuclear stains or fluorescent protein-based nuclear reporters. We also share a diverse high-quality training dataset of about 10,000 nuclei. Furthermore, we advanced the MorphoGraphX analysis and visualization software by, among other things, provi

SUBMITTER: Vijayan A 

PROVIDER: S-EPMC11273294 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature