title: Optimal multiple surface segmentation with shape and context priors.
abstract: Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary evidence, large object deformations, and mutual influence between adjacent objects. This paper reports a novel approach to multi-object segmentation that incorporates both shape and context prior knowledge in a 3-D graph-theoretic framework to help overcome the stated challenges. We employ an arc-based graph representation to incorporate a wide spectrum of prior information through pair-wise energy terms. In particular, a shape-prior term is used to penalize local shape changes and a context-prior term is used to penalize local surface-distance changes from a model of the expected shape and surface distances, respectively. The globally optimal solution for multiple surfaces is obtained by computing a maximum flow in a low-order polynomial time. The proposed method was validated on intraretinal layer segmentation of optical coherence tomography images and demonstrated statistically significant improvement of segmentation accuracy compared to our earlier graph-search method that was not utilizing shape and context priors. The mean unsigned surface positioning errors obtained by the conventional graph-search approach (6.30 ±1.58 μ m) was improved to 5.14±0.99 μ m when employing our new method with shape and context priors.
authors.affiliation: Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA. song@ge.com
authors.forename: Qi
authors.initials: Q
authors.lastname: Song
authors.forename: Junjie
authors.initials: J
authors.lastname: Bai
authors.forename: Mona K
authors.initials: MK
authors.lastname: Garvin
authors.forename: Milan
authors.initials: M
authors.lastname: Sonka
authors.forename: John M
authors.initials: JM
authors.lastname: Buatti
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique