title: Adapting Active Shape Models for 3D segmentation of tubular structures in medical images.
abstract: Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, The Netherlands.
authors.forename: Marleen
authors.initials: M
authors.lastname: de Bruijne
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
authors.forename: Wiro J
authors.initials: WJ
authors.lastname: Niessen
mesh_terms: D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods; D001012:Aorta, Abdominal / Q000000981:diagnostic imaging*; D017544:Aortic Aneurysm, Abdominal / Q000000981:diagnostic imaging*; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique