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) Society
• Contains state-of-the-art technical approaches to key challenges
• Demonstrates proven algorithms for a whole range of essential medical imaging applications
• Includes source codes for use in a plug-and-play manner
• Embraces future directions in the fields of medical image computing and computer-assisted intervention

Table of Contents

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