LOCALLY ADAPTIVE AUTOREGRESSIVE ACTIVE MODELS FOR SEGMENTATION OF 3D ANATOMICAL STRUCTURES
Charles Florin, Nikos Paragios, Gareth Funka-Lea, Jim Williams · 2007
Many techniques of knowledge-based segmentation consist of building statistical models that describe the deformations of the structure of interest, and then fit these models to the image data. In this paper, we introduce a novel family of shape prior models that aim to capture such varying support. To this end, 3D segmentation is considered by modeling the relationship between contours on consecutive slices using autoregression. Then, the segmentation is performed progressively on the 2D slices in a qualitative fashion, starting from the ones with strong data support toward the ones of limited support. Successive segmentation maps are linked through a locally adaptive autoregressive prediction mechanism - that is learned through training - where confidence of the data from prior slices constrains the results. Such prediction is integrated with a contour minimization technique, leading to a Bayesian sequential procedure that iteratively predicts and corrects 2D contours leading to complete reconstruction of 3D anatomical structures. A quantitative comparative study with 3D active shape models demonstrate the potential of the method.