Computational Methods and Clinical Applications for Spine Imaging

Chapter 1 Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

Chapter 2 Machine Learning Based Bone Segmentation in Ultrasound

Chapter 3 Variational Segmentation of the White and Gray Matter in the Spinal Cord Using a Shape Prior

Chapter 4 Automated Intervertebral Disc Segmentation Using Deep Convolutional Neural Networks

Chapter 5 Fully Automatic Localisation of Vertebrae in CT Images Using Random Forest Regression Voting

Chapter 6 Global Localization and Orientation of the Cervical Spine in X-ray Images

Chapter 7 Accurate Intervertebral Disc Localisation and Segmentation in MRI Using Vantage Point Hough Forests and Multi-atlas Fusion

Chapter 8 Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation

Chapter 9 Fully Automatic Localization and Segmentation of Intervertebral Disc from 3D Multi-modality MR Images by Regression Forest and CNN

Chapter 10 Manual and Computer-Assisted Pedicle Screw Placement Plans: A Quantitative Comparison

Chapter 11 Detection of Degenerative Osteophytes of the Spine on PET/CT Using Region-Based Convolutional Neural Networks

Chapter 12 Reconstruction of 3D Lumvar Vertebra from Two X-ray Images Based on 2D/3D Registration

Chapter 13 Classification of Progressive and Non-progressive Scoliosis Patients Using Discriminant Manifolds