We developed a hierarchical deep learning shape model-driven method to automatically track the motion of the heart, a complex and highly deformable organ, on two-dimensional cine MRI images. The deep-learning shape model was trained based on a Deep Boltzmann Machine (DBM)1,2 to characterize both global and local shape properties of the heart for accurate heart segmentation on each cine frame. Preliminary experimental results demonstrate the superior shape tracking performance of our proposed method versus two other methods. The tracking method is designed for heart motion pattern analysis during MRI-guided radiotherapy and the subsequent evaluation of potential heart toxicity from radiotherapy.
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