title: 3D cell nuclei segmentation based on gradient flow tracking.
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.
  
  
  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%.
  
  
  CONCLUSIONS
  The proposed algorithm is able to segment closely juxtaposed or touching cell nuclei obtained from 3D microscopy imaging with reasonable accuracy.
authors.affiliation: Center for Bioinformatics, Harvard Center for Neurodegeneration and Repair, Harvard Medical School, Boston, MA, USA. gli@bwh.harvard.edu
authors.forename: Gang
authors.initials: G
authors.lastname: Li
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
authors.forename: Ashley
authors.initials: A
authors.lastname: Tarokh
authors.forename: Jingxin
authors.initials: J
authors.lastname: Nie
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.forename: Andrew
authors.initials: A
authors.lastname: Mara
authors.forename: Scott
authors.initials: S
authors.lastname: Holley
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D000818:Animals; D002467:Cell Nucleus / Q000648:ultrastructure*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*