@article {69568, title = {DaCapo: a modular deep learning framework for scalable 3D image segmentation}, journal = {arXiv}, year = {2024}, month = {2024 Aug 05}, abstract = {

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo{\textquoteright}s unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

}, doi = {10.48550/arXiv.2408.02834}, url = {https://doi.org/10.48550/arXiv.2408.02834}, author = {Patton, William and Jeff L Rhoades and Zouinkhi, Marwan and Ackerman, David G and Malin-Mayor, Caroline and Adjavon, Diane and Heinrich, Larissa and Bennett, Davis and Zubov, Yurii and Team, CellMap Project and Weigel, Aubrey and Funke, Jan} } @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} }