title: Choroidal vasculature characteristics based choroid segmentation for enhanced depth imaging optical coherence tomography images.
abstract: OBJECTIVE
  In clinical research, it is important to measure choroidal thickness when eyes are affected by various diseases. The main purpose is to automatically segment choroid for enhanced depth imaging optical coherence tomography (EDI-OCT) images with five B-scans averaging.
  
  
  METHODS
  The authors present an automated choroid segmentation method based on choroidal vasculature characteristics for EDI-OCT images with five B-scans averaging. By considering the large vascular of the Haller's layer neighbor with the choroid-sclera junction (CSJ), the authors measured the intensity ascending distance and a maximum intensity image in the axial direction from a smoothed and normalized EDI-OCT image. Then, based on generated choroidal vessel image, the authors constructed the CSJ cost and constrain the CSJ search neighborhood. Finally, graph search with smooth constraints was utilized to obtain the CSJ boundary.
  
  
  RESULTS
  Experimental results with 49 images from 10 eyes in 8 normal persons and 270 images from 57 eyes in 44 patients with several stages of diabetic retinopathy and age-related macular degeneration demonstrate that the proposed method can accurately segment the choroid of EDI-OCT images with five B-scans averaging. The mean choroid thickness difference and overlap ratio between the authors' proposed method and manual segmentation drawn by experts were -11.43 μm and 86.29%, respectively.
  
  
  CONCLUSIONS
  Good performance was achieved for normal and pathologic eyes, which proves that the authors' method is effective for the automated choroid segmentation of the EDI-OCT images with five B-scans averaging.
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Chen
authors.affiliation: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
authors.forename: Sijie
authors.initials: S
authors.lastname: Niu
authors.affiliation: Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.
authors.forename: Songtao
authors.initials: S
authors.lastname: Yuan
authors.affiliation: Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Fan
authors.affiliation: Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China.
authors.forename: Qinghuai
authors.initials: Q
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D002829:Choroid / Q000098:blood supply* / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D018919:Neovascularization, Physiologic; D059629:Signal-To-Noise Ratio; D041623:Tomography, Optical Coherence