Computational Methods and Clinical Applications in Musculoskeletal Imaging

<div>Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps.- Shape-aware Deep Convolutional Neural Network for Vertebrae Segmentation.- Automated Characterization of Body Composition and Frailty with Clinically Acquired CT.- Unfolded cylindrical projection for rib fracture diagnosis.- 3D Cobb Angle Measurements from Scoliotic Mesh Models with Varying Face-Vertex Density.- Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features.- Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients.- Reconstruction of 3D muscle fiber structure using high resolution cryosectioned volume.- Segmentation of Pathological Spines in CT Images Using a Two-Way CNN and a Collision-Based Model.- Attention-driven deep learning for pathological spine segmentation.- Automatic Full Femur Segmentation from Computed Tomography Datasets using an Atlas-Based Approach.- Classification of Osteoporotic Vertebral Fractures using Shape and Appearance Modelling.- DSMS-FCN: A Deeply Supervised Multi-Scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images.</div><div><br/></div>