Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations (original) (raw)

New Results

doi: https://doi.org/10.1101/2021.07.28.454016

Abstract

We present a method for automated nucleus identification and tracking in time-lapse microscopy recordings of entire developing embryos. Our method combines deep learning and global optimization to enable complete lineage reconstruction from sparse point annotations, and uses parallelization to process multi-terabyte light-sheet recordings, which we demonstrate on three common model organisms: mouse, zebrafish, Drosophila. On the most difficult dataset (mouse), our method correctly reconstructs 75.8% of cell lineages spanning 1 hour, compared to 31.8% for the previous state of the art, thus enabling biologists to determine where and when cell fate decisions are made in developing embryos, tissues, and organs.

Competing Interest Statement

The authors have declared no competing interest.

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