title: Deformable segmentation via sparse shape representation.
abstract: Appearance and shape are two key elements exploited in medical image segmentation. However, in some medical image analysis tasks, appearance cues are weak/misleading due to disease/artifacts and often lead to erroneous segmentation. In this paper, a novel deformable model is proposed for robust segmentation in the presence of weak/misleading appearance cues. Owing to the less trustable appearance information, this method focuses on the effective shape modeling with two contributions. First, a shape composition method is designed to incorporate shape prior on-the-fly. Based on two sparsity observations, this method is robust to false appearance information and adaptive to statistically insignificant shape modes. Second, shape priors are modeled and used in a hierarchical fashion. More specifically, by using affinity propagation method, our deformable surface is divided into multiple partitions, on which local shape models are built independently. This scheme facilitates a more compact shape prior modeling and hence a more robust and efficient segmentation. Our deformable model is applied on two very diverse segmentation problems, liver segmentation in PET-CT images and rodent brain segmentation in MR images. Compared to state-of-art methods, our method achieves better performance in both studies.
authors: Siemens Medical Solutions, Malvern, PA, USA.
authors: Shaoting
authors: S
authors: Zhang
authors: Yiqiang
authors: Y
authors: Zhan
authors: Maneesh
authors: M
authors: Dewan
authors: Junzhou
authors: J
authors: Huang
authors: Dimitris N
authors: DN
authors: Metaxas
authors: Xiang Sean
authors: XS
authors: Zhou
mesh_terms: D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D002531:Cerebellum / Q000473:pathology; D003198:Computer Simulation; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000473:pathology; D008168:Lung / Q000473:pathology; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods; D051381:Rats; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods