Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations
Item Type: | Preprint |
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Title: | Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations |
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Creators Name: | Malin-Mayor, C. and Hirsch, P. and Guignard, L. and McDole, K. and Wan, Y. and Lemon, W.C. and Keller, P.J. and Preibisch, S. and 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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Keywords: | Animals, Drosophila, Mice, Zebrafish |
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Source: | bioRxiv |
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Publisher: | Cold Spring Harbor Laboratory Press |
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Article Number: | 2021.07.28.454016 |
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Date: | 29 July 2021 |
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Official Publication: | https://doi.org/10.1101/2021.07.28.454016 |
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Related to: | |
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