Main Menu (Mobile)- Block

Main Menu - Block

custom | custom

Search Results

filters_region_cap | custom

Filter

facetapi-Q2b17qCsTdECvJIqZJgYMaGsr8vANl1n | block

Associated Lab

facetapi-PV5lg7xuz68EAY8eakJzrcmwtdGEnxR0 | block

Publication Date

general_search_page-panel_pane_1 | views_panes

2 Janelia Publications

Showing 1-2 of 2 results
Your Criteria:
    Keller LabFunke Lab
    View PDF
    09/05/22 | Automated reconstruction of whole-embryo cell lineages by learning from sparse annotations.
    Malin-Mayor C, Hirsch P, Guignard L, McDole K, Wan Y, Lemon WC, Kainmueller D, Keller PJ, Preibisch S, Funke J
    Nature Biotechnology. 2022 Sep 05:. doi: 10.1038/s41587-022-01427-7

    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.

    View Publication Page
    Funke Lab
    09/22/22 | Tracking by Weakly-Supervised Learning and Graph Optimization for Whole-Embryo C. elegans lineages
    Wang L, Dou Q, Fletcher PT, Speidel S, Li S
    International Conference on Medical Image Computing and Computer-Assisted Intervention. 2022 Sep 16:. doi: 10.1007/978-3-031-16440-8

    Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nuclei center point annotations with an integer linear program (ILP) for optimal cell lineage extraction. Our work specifically addresses the following challenging properties of C. elegans embryo recordings: (1) Many cell divisions as compared to benchmark recordings of other organisms, and (2) the presence of polar bodies that are easily mistaken as cell nuclei. To cope with (1), we devise and incorporate a learnt cell division detector. To cope with (2), we employ a learnt polar body detector. We further propose automated ILP weights tuning via a structured SVM, alleviating the need for tedious manual set-up of a respective grid search.

    View Publication Page