Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.

ABSTRACT: Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses requires experimental tools to measure these cues. In the context of large scale tissues, this can be achieved by segmenting individual cells to extract their shapes and deformations which in turn inform on their mechanical environment. Historically, this has been done by segmentation methods which are well known to be time consuming and error prone. In this context however, one doesn't necessarily require a cell-level description and a coarse-grained approach can be more efficient while using tools different from segmentation. The advent of mac

SUBMITTER: Combe L 

PROVIDER: S-EPMC9934364 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature