Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.

Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) SocietyContains state-of-the-art technical approaches to key challengesDemonstrates proven algorithms for a whole range of essential medical imaging applicationsIncludes source codes for use in a plug-and-play mannerEmbraces future directions in the fields of medical image computing and computer-assisted intervention

Medical Image Computing 1. Image Synthesis and Super-resolution in Medical Imaging 2. Machine Learning for Image Reconstruction 3. Liver Lesion Detection in CT using Deep Learning Techniques 4. Computer Aided Diagnosis in Lung 5. Text Mining and Deep Learning for Disease Classification 6. Multi-Atlas Segmentation 7. Segmentation using Adversarial Image-to-image Networks 8. Multimodal Medical Volumes Translation and Segmentation with Generative Adversarial Network 9. Landmark Detection and Multi-organ Segmentation: Representations and Supervised Approaches 10. Deep Multi-level Contextual Networks for Biomedical Image Segmentation 11. LOGISMOS-JEI: Segmentation Using Optimal Graph Search and Just-Enough Interaction 12. Deformable Models, Sparsity and Learning-Based Segmentation for Cardiac MRI Based Analytics 13. Image Registration with Sliding Motion 14. Image Registration Using Machine and Deep Learning 15. Imaging Biomarkers in Alzheimer's Disease 16. Machine Learning Based Imaging Biomarkers in Large Scale Population Studies: a Neuroimaging Perspective 17. Imaging Biomarkers for Cardiovascular Disease 18. Radiomics: Data Mining Using Quantitative Medical Image Features 19. Random Forests in Medical Image Computing 20. Convolutional Neural Networks 21. Deep Learning: RNNs and LSTM 22. Deep Multiple Instance Learning for Digital Histopathology 23. Deep Learning: Generative Adversarial Networks and Adversarial Methods 24. Linear Statistical Shape Models and Landmark Location

Computer Assisted Interventions 25. Computer-Integrated Interventional Medicine: A 30-year Perspective 26. Technology and Applications in Interventional Imaging: 2D X-Ray Radiography / Fluoroscopy and 3D Cone-Beam CT 27. Interventional Imaging: MR 28. Interventional Imaging: Ultrasound 29. Interventional Imaging: Vision 30. Interventional Imaging: Biophotonics 31. External Tracking Devices and Tracked Tool Calibration 32. Image-Based Surgery Planning 33. Human-Machine Interfaces for Medical Imaging and Clinical Interventions 34. Robotic Interventions 35. Systems Integration 36. Clinical Translation 37. Interventional Procedures Training 38. Surgical Data Science 39. Computational Biomechanics for Medical Imaging 40. Challenges in Computer Assisted Interventions