title: Geodesic patch-based segmentation.
abstract: Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.
authors.forename: Zehan
authors.initials: Z
authors.lastname: Wang
authors.forename: Kanwal K
authors.initials: KK
authors.lastname: Bhatia
authors.forename: Ben
authors.initials: B
authors.lastname: Glocker
authors.forename: Antonio
authors.initials: A
authors.lastname: Marvao
authors.forename: Tim
authors.initials: T
authors.lastname: Dawes
authors.forename: Kazunari
authors.initials: K
authors.lastname: Misawa
authors.forename: Kensaku
authors.initials: K
authors.lastname: Mori
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
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