title: Contour-Driven Atlas-Based Segmentation.
abstract: We propose new methods for automatic segmentation of images based on an atlas of manually labeled scans and contours in the image. First, we introduce a Bayesian framework for creating initial label maps from manually annotated training images. Within this framework, we model various registration- and patch-based segmentation techniques by changing the deformation field prior. Second, we perform contour-driven regression on the created label maps to refine the segmentation. Image contours and image parcellations give rise to non-stationary kernel functions that model the relationship between image locations. Setting the kernel to the covariance function in a Gaussian process establishes a distribution over label maps supported by image structures. Maximum a posteriori estimation of the distribution over label maps conditioned on the outcome of the atlas-based segmentation yields the refined segmentation. We evaluate the segmentation in two clinical applications: the segmentation of parotid glands in head and neck CT scans and the segmentation of the left atrium in cardiac MR angiography images.
authors.forename: Christian
authors.initials: C
authors.lastname: Wachinger
authors.forename: Karl
authors.initials: K
authors.lastname: Fritscher
authors.forename: Greg
authors.initials: G
authors.lastname: Sharp
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
mesh_terms: D001499:Bayes Theorem; D016208:Databases, Factual; D006325:Heart Atria / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D009333:Neck / Q000000981:diagnostic imaging; D010306:Parotid Gland / Q000000981:diagnostic imaging; D014057:Tomography, X-Ray Computed