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.

Table of Contents

• Contributors
• Acknowledgment
• 1. Image synthesis and superresolution in medical imaging
• 2. Machine learning for image reconstruction
• 3. Liver lesion detection in CT using deep learning techniques
• 4. CAD in lung
• 5. Text mining and deep learning for disease classification
• 6. Multiatlas segmentation
• 7. Segmentation using adversarial image-to-image networks
• 8. Multimodal medical volumes translation and segmentation with generative adversarial network
• 9. Landmark detection and multiorgan segmentation: Representations and supervised approaches
• 10. Deep multilevel 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 diseases
• 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
• 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. System integration
• 36. Clinical translation
• 37. Interventional procedures training
• 38. Surgical data science
• 39. Computational biomechanics for medical image analysis
• 40. Challenges in Computer Assisted Interventions: Challenges in design, development, evaluation, and clinical deployment of Computer Assisted Intervention solutions