Handbook of Medical Image Computing and Computer Assisted Intervention

S. Kevin Zhou, Daniel Rueckert, Gabor Fichtinger

• Publisher: Academic Press
• Year: 2019
• Print ISBN:
  9780128161760
• eBook ISBN:
  9780128165867
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

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

• Title of Book
• Chapter 1: Image synthesis and superresolution in medical imaging
• Chapter 2: Machine learning for image reconstruction
• Chapter 3: Liver lesion detection in CT using deep learning techniques
• Chapter 4: CAD in lung
• Chapter 5: Text mining and deep learning for disease classification
• Chapter 6: Multiatlas segmentation
• Chapter 7: Segmentation using adversarial image-to-image networks
• Chapter 8: Multimodal medical volumes translation and segmentation with generative adversarial network
• Chapter 9: Landmark detection and multiorgan segmentation: Representations and supervised approaches
• Chapter 10: Deep multilevel contextual networks for biomedical image segmentation
• Chapter 11: LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction
• Chapter 12: Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
• Chapter 13: Image registration with sliding motion
• Chapter 14: Image registration using machine and deep learning
• Chapter 15: Imaging biomarkers in Alzheimer's disease
• Chapter 16: Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
• Chapter 17: Imaging biomarkers for cardiovascular diseases
• Chapter 18: Radiomics
• Chapter 19: Random forests in medical image computing
• Chapter 20: Convolutional neural networks
• Chapter 21: Deep learning: RNNs and LSTM
• Chapter 22: Deep multiple instance learning for digital histopathology
• Chapter 23: Deep learning: Generative adversarial networks and adversarial methods
• Chapter 24: Linear statistical shape models and landmark location
• Chapter 25: Computer-integrated interventional medicine: A 30 year perspective
• Chapter 26: Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT
• Chapter 27: Interventional imaging: MR
• Chapter 28: Interventional imaging: Ultrasound
• Chapter 29: Interventional imaging: Vision
• Chapter 30: Interventional imaging: Biophotonics
• Chapter 31: External tracking devices and tracked tool calibration
• Chapter 32: Image-based surgery planning
• Chapter 33: Human–machine interfaces for medical imaging and clinical interventions
• Chapter 34: Robotic interventions
• Chapter 35: System integration
• Chapter 36: Clinical translation
• Chapter 37: Interventional procedures training
• Chapter 38: Surgical data science
• Chapter 39: Computational biomechanics for medical image analysis
• Chapter 40: Challenges in Computer Assisted Interventions
• Index