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Nu3D: 3D Nuclei Segmentation from Light-Sheet Microscopy Images of the Embryonic Heart

Abstract : In developmental biology, quantification of cellular morphological features is a critical step to get insight into tissue morphogenesis. The efficacy of the quantification tools rely heavily on robust segmentation techniques which can delineate individual cells/nuclei from cluttered environment. Application of popular neural network methods is restrained by the availability of ground truth necessary for 3D nuclei segmentation. Consequently, we propose a convolutional neural network method, combined with graph theoretic approach for 3D nuclei segmentation of mouse embryo cardiomyocytes, imaged by light-sheet microscopy. The designed neural network architecture encapsulates both membrane and nuclei cues for 2D detection. A global association of the 2D nuclei detection is performed by solving a linear optimization with second order constraint to obtain 3D nuclei reconstruction.
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Contributor : Sigolène Meilhac Connect in order to contact the contributor
Submitted on : Monday, November 14, 2022 - 12:25:46 PM
Last modification on : Saturday, November 19, 2022 - 3:58:38 AM




Rituparna Sarkar, Daniel Darby, Heloise Foucambert, Sigolène Meilhac, Jean-Christophe Olivo-Marin. Nu3D: 3D Nuclei Segmentation from Light-Sheet Microscopy Images of the Embryonic Heart. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), IEEE, Apr 2021, Nice, France. pp.929-933, ⟨10.1109/ISBI48211.2021.9433987⟩. ⟨pasteur-03851169⟩



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