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.

Share this

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