Deep neural networks to register and annotate cells in moving and deforming nervous systems.

ABSTRACT: Aligning and annotating the heterogeneous cell types that make up complex cellular tissues remains a major challenge in the analysis of biomedical imaging data. Here, we present a series of deep neural networks that allow for automatic non-rigid registration and cell identification, developed in the context of freely moving and deforming invertebrate nervous systems. A semi-supervised learning approach was used to train a Caenorhabditis elegans registration network (BrainAlignNet) that aligns pairs of images of the bending C. elegans head with single-pixel-level accuracy. When incorporated into an image analysis pipeline, this network can link neurons over time with 99.6% accuracy. This network could also be readily purposed to align neurons from the jellyfish Clytia hemis

SUBMITTER: Atanas AA 

PROVIDER: S-EPMC12912725 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature