@article {68587, title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.}, journal = {Nature Biotechnology}, volume = {41}, year = {2023}, month = {2023 Jan 01}, pages = {44-49}, abstract = {

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8\% of cell lineages spanning 1 h, as compared to 31.8\% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

}, keywords = {Animals, Blastocyst, Cell Lineage, Embryo, Mammalian, Mice, Microscopy}, issn = {1546-1696}, doi = {10.1038/s41587-022-01427-7}, author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan} } @article {68313, title = {Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.}, journal = {Nature Biotechnology}, year = {2022}, month = {2022 Sep 05}, abstract = {

We present a method to automatically identify and track nuclei in time-lapse microscopy recordings of entire developing embryos. The method combines deep learning and global optimization. On a mouse dataset, it reconstructs 75.8\% of cell lineages spanning 1 h, as compared to 31.8\% for the competing method. Our approach improves understanding of where and when cell fate decisions are made in developing embryos, tissues, and organs.

}, issn = {1546-1696}, doi = {10.1038/s41587-022-01427-7}, author = {Malin-Mayor, Caroline and Hirsch, Peter and Guignard, Leo and McDole, Katie and Wan, Yinan and Lemon, William C and Kainmueller, Dagmar and Keller, Philipp J and Preibisch, Stephan and Funke, Jan} } @article {47987, title = {Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms.}, journal = {Nature Biotechnology}, volume = {34}, year = {2016}, month = {2016 Oct 31}, pages = {1267-78}, abstract = {

Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.

}, issn = {1546-1696}, doi = {10.1038/nbt.3708}, author = {Royer, Lo{\"\i}c A and Lemon, William C and Chhetri, Raghav K and Wan, Yinan and Coleman, Michael and Myers, Eugene W and Keller, Philipp J} }