Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning.

ABSTRACT: Phenotypic profiling of large three-dimensional microscopy data sets has not been widely adopted due to the challenges posed by cell segmentation and feature selection. The computational demands of automated processing further limit analysis of hard-to-segment images such as of neurons and organoids. Here we describe a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and advanced data visualization. We demonstrate the analysis potential on complex 3D images by investigating the phenotypic alterations of: neurons in response to apoptosis-inducing treatments and morphogenesis for oncogene-expressing hu

SUBMITTER: Mergenthaler P 

PROVIDER: S-EPMC7932518 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature