We develop a novel deformable atlas method for multi- structure segmentation that seamlessly combines the advantages of image-based and atlas-based methods. The method formulates a proba- bilistic 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 bound- aries, and is robust to large anatomical variation. The proposed method was applied to segment multiple structures in both normal and dis- eased brains and was shown to significantly improve results especially in diseased brains.
['Xiaofeng Liu', 'Albert Montillo', 'Ek Tsoon Tan', 'John F. Schenck', 'Paulo R. S. Mendonça']
Deformable Atlas for Multi-structure Segmentation