The MICCAI Book Series: Handbook of Medical Image Computing and Computer Assisted Intervention
The MICCAI Book Series: Handbook of Medical Image Computing and Computer Assisted Intervention
Editor: Zhou, S. Kevin, Rueckert, Daniel and Fichtinger, Gabor
Publication Year: 2020
Publisher: Elsevier Science & Technology
Single-User Purchase Price:
$225.00

Unlimited-User Purchase Price:
$337.50
ISBN: 978-0-12-816176-0
Category: Health & Medicine - Medicine
Image Count:
375
Book Status: Available
Table of Contents
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