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Abstract #1414

Super Resolution Segmentation of the Scapula from Clinical MRI

Francesco Caliva1, Victoria Wong1, Favian Su1, Drew Lansdown1, and Valentina Pedoia1
1University of California San Francisco, San Francisco, CA, United States


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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