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bioRxiv · 10.1101/2021.07.28.454016

Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations

Malin-Mayor, C.·Hirsch, P.·Guignard, L.·McDole, K.·Wan, Y.·Lemon, W. C.·Keller, P. J.·Preibisch, S.·Funke, J.
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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.

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Malin-Mayor, C., Hirsch, P., Guignard, L., McDole, K., Wan, Y., Lemon, W. C., Keller, P. J., Preibisch, S., Funke, J.. 2021-07-29. Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations. https://doi.org/10.1101/2021.07.28.454016

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