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3D cell nuclei segmentation based on gradient flow tracking

Gang Li12, Tianming Liu13, Ashley Tarokh13, Jingxin Nie12, Lei Guo2, Andrew Mara4, Scott Holley4 and Stephen TC Wong13*

Author Affiliations

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 Functional and Molecular Imaging Center, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA

4 Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT, USA

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BMC Cell Biology 2007, 8:40  doi:10.1186/1471-2121-8-40

Published: 4 September 2007



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.


Both qualitative and quantitative results on synthesized and original 3D images are provided to demonstrate the performance and generality of the proposed method. Both the over-segmentation and under-segmentation percentages of the proposed method are around 5%. The volume overlap, compared to expert manual segmentation, is consistently over 90%.


The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.