title: Deformable atlas for multi-structure segmentation.
abstract: We develop a novel deformable atlas method for multistructure segmentation that seamlessly combines the advantages of image-based and atlas-based methods. The method formulates a probabilistic framework that combines prior anatomical knowledge with image-based cues that are specific to the subject's anatomy, and solves it using expectation-maximization method. It improves the segmentation over conventional label fusion methods especially around the structure boundaries, and is robust to large anatomical variation. The proposed method was applied to segment multiple structures in both normal and diseased brains and was shown to significantly improve results especially in diseased brains.
authors: General Electric Global Research Center, Niskayuna, NY USA. xiaofeng.liu@ge.com
authors: Xiaofeng
authors: X
authors: Liu
authors: General Electric Global Research Center, Niskayuna, NY USA.
authors: Albert
authors: A
authors: Montillo
authors: General Electric Global Research Center, Niskayuna, NY USA.
authors: Ek T
authors: ET
authors: Tan
authors: General Electric Global Research Center, Niskayuna, NY USA.
authors: John F
authors: JF
authors: Schenck
authors: General Electric Global Research Center, Niskayuna, NY USA.
authors: Paulo
authors: P
authors: Mendonca
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique