CELLECT: contrastive embedding learning for large-scale efficient cell tracking.

ABSTRACT: Quantitative analysis of large-scale cellular behaviors plays an increasingly crucial role in understanding mechanisms of diverse physiopathological processes, but achieving cell tracking with both high performance and efficiency in practical applications remains a challenge. Here we introduce CELLECT, a contrastive embedding learning method for large-scale efficient cell tracking, and demonstrate it on the Caenorhabditis elegans dataset in the Cell Tracking Challenge. By contrastive learning of latent embeddings of diverse cellular structures, a CELLECT model pretrained on a single public dataset can be effectively applied across different imaging modalities and species with broad generalization. Using advanced two-photon imaging, CELLECT enables real-time 3D tracking of large-scale B cel

SUBMITTER: Zhou H 

PROVIDER: S-EPMC12615263 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature