3D cell nuclei segmentation based on gradient flow tracking |
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| Authors: | Gang Li Tianming Liu Ashley Tarokh Jingxin Nie Lei Guo Andrew Mara Scott Holley Stephen TC Wong |
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| Institution: | (1) Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, Boston, MA, USA;(2) School of Automation, Northwestern Polytechnic University, Xi'an, China;(3) Department of Radiology, Brigham and Women's Hospital, Functional and Molecular Imaging Center, Boston, MA, USA;(4) Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, USA |
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| Abstract: | Background Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological
studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was
designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation
approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive
thresholding. |
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