title: Organ segmentation with level sets using local shape and appearance priors.
abstract: Organ segmentation is a challenging problem on which recent progress has been made by incorporation of local image statistics that model the heterogeneity of structures outside of an organ of interest. However, most of these methods rely on landmark based segmentation, which has certain drawbacks. We propose to perform organ segmentation with a novel level set algorithm that incorporates local statistics via a highly efficient point tracking mechanism. Specifically, we compile statistics on these tracked points to allow for a local intensity profile outside of the contour and to allow for a local surface area penalty, which allows us to capture fine detail where it is expected. The local intensity and curvature models are learned through landmarks automatically embedded on the surface of the training shapes. We use Parzen windows to model the internal organ intensities as one distribution since this is sufficient for most organs. In addition, since the method is based on level sets, we are able to naturally take advantage of recent work on global shape regularization. We show state-of-the-art results on the challenging problems of liver and kidney segmentation.
authors.affiliation: Siemens Corporate Research, Imaging and Visualization Dept., Princeton, USA.
authors.forename: Timo
authors.initials: T
authors.lastname: Kohlberger
authors.forename: M Gökhan
authors.initials: MG
authors.lastname: Uzunba
authors.forename: Christopher
authors.initials: C
authors.lastname: Alvino
authors.forename: Timor
authors.initials: T
authors.lastname: Kadir
authors.forename: Daniel O
authors.initials: DO
authors.lastname: Slosman
authors.forename: Gareth
authors.initials: G
authors.lastname: Funka-Lea
mesh_terms: D000465:Algorithms; D006801:Humans; D007668:Kidney / Q000000981:diagnostic imaging*; D008099:Liver / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*