Methodology article
3D cell nuclei segmentation based on gradient flow tracking
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* Corresponding author: Stephen TC Wong wong@crystal.harvard.edu
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
BMC Cell Biology 2007, 8:40 doi:10.1186/1471-2121-8-40
Published: 4 September 2007Abstract
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.
Results
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%.
Conclusion
The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.