Computational Methods and Clinical Applications in Musculoskeletal Imaging

Chapter 1 Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps

Chapter 2 Shape-Aware Deep Convolutional Neural Network for Vertebrae Segmentation

Chapter 3 Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

Chapter 4 Unfolded Cylindrical Projection for Rib Fracture Diagnosis

Chapter 5 3D Cobb Angle Measurements from Scoliotic Mesh Models with Varying Face-Vertex Density

Chapter 6 Automatic Localization of the Lumbar Vertebral Landmarks in CT Images with Context Features

Chapter 7 Joint Multimodal Segmentation of Clinical CT and MR from Hip Arthroplasty Patients

Chapter 8 Reconstruction of 3D Muscle Fiber Structure Using High Resolution Cryosectioned Volume

Chapter 9 Segmentation of Pathological Spines in CT Images Using a Two-Way CNN and a Collision-Based Model

Chapter 10 Attention-Driven Deep Learning for Pathological Spine Segmentation

Chapter 11 Automatic Full Femur Segmentation from Computed Tomography Datasets Using an Atlas-Based Approach

Chapter 12 Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling

Chapter 13 DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images