We propose a deep learning approach to simultaneous super resolution and segmentation. We experiment our framework on the challenging task of generating 3D high-resolution axial shoulder MRI from 2D clinical MRI sequences, and demonstrate the ability to produce precise 3D scapula bone models. With an extensive experimental study, we show that with super-resolution, it is possible to produce high resolution bone models, which are invaluable for surgeons in the pre-operative planning of patients with various shoulder conditions. This method has the potential to reduce patients' need for undergoing CT scans, hence preventing exposure to radiation.
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