210-CELLECT: Efficient Contrastive Cell Tracking
The article introduces CELLECT, a novel deep learning method for contrastive embedding learning designed for large-scale, efficient cell tracking. The authors developed CELLECT to overcome the challenges of high-performance and high-efficiency tracking in massive three-dimensional (3D) time-lapse microscopy datasets by relying on sparse annotations instead of extensive manual segmentation. The system employs contrastive learning to extract robust latent embeddings of diverse cellular structures, demonstrating broad generalization across different imaging modalities and species, including…
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