title: A generative model for OCT retinal layer segmentation by integrating graph-based multi-surface searching and image registration.
abstract: We proposed a generative probabilistic modeling framework for automated segmentation of retinal layers from Optical Coherence Tomography (OCT) data. The objective is to learn a segmentation protocol from a collection of training images that have been manually labeled. Our model results in a novel OCT retinal layer segmentation approach which integrates algorithms of simultaneous searching of multiple interacting layer interfaces, image registration and machine learning. Different from previous work, our approach combines the benefits of constraining spatial layout of retinal layers, using a set of more robust local image descriptors, employing a mechanism for learning from manual labels and incorporating the inter-subject anatomical similarities of retina. With a set of OCT volumetric images from mutant canine retinas, we experimentally validated that our approach outperforms two state-of-the-art techniques.
authors: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
authors: Yuanjie
authors: Y
authors: Zheng
authors: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors: Rui
authors: R
authors: Xiao
authors: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
authors: Yan
authors: Y
authors: Wang
authors: Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
authors: James C
authors: JC
authors: Gee
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004285:Dogs; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012160:Retina / Q000473:pathology*; D012162:Retinal Degeneration / Q000473:pathology*; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D018511:Systems Integration; D041623:Tomography, Optical Coherence / Q000379:methods*