Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention

1st Edition - October 18, 2019

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  • Editors: Kevin Zhou, Daniel Rueckert, Gabor Fichtinger
  • eBook ISBN: 9780128165867
  • Hardcover ISBN: 9780128161760

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Description

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.

Key Features

  • 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

    Readership

    Researchers, graduate students and practitioners in medical imaging, computer assisted intervention, computer vision and biomedical engineering

    Table of Contents

    • Cover image
    • Title page
    • Table of Contents
    • Copyright
    • Contributors
    • Acknowledgment
    • Chapter 1: Image synthesis and superresolution in medical imaging
    • Abstract
    • 1.1. Introduction
    • 1.2. Image synthesis
    • 1.3. Superresolution
    • 1.4. Conclusion
    • References
    • Chapter 2: Machine learning for image reconstruction
    • Abstract
    • Acknowledgements
    • 2.1. Inverse problems in imaging
    • 2.2. Unsupervised learning in image reconstruction
    • 2.3. Supervised learning in image reconstruction
    • 2.4. Training data
    • 2.5. Loss functions and evaluation of image quality
    • 2.6. Discussion
    • References
    • Chapter 3: Liver lesion detection in CT using deep learning techniques
    • Abstract
    • Acknowledgements
    • 3.1. Introduction
    • 3.2. Fully convolutional network for liver lesion detection in CT examinations
    • 3.3. Fully convolutional network for CT to PET synthesis to augment malignant liver lesion detection
    • 3.4. Discussion and conclusions
    • References
    • Chapter 4: CAD in lung
    • Abstract
    • 4.1. Overview
    • 4.2. Origin of lung CAD
    • 4.3. Lung CAD systems
    • 4.4. Localized disease
    • 4.5. Diffuse lung disease
    • 4.6. Anatomical structure extraction
    • References
    • Chapter 5: Text mining and deep learning for disease classification
    • Abstract
    • Acknowledgements
    • 5.1. Introduction
    • 5.2. Literature review
    • 5.3. Case study 1: text mining in radiology reports and images
    • 5.4. Case study 2: text mining in pathology reports and images
    • 5.5. Conclusion and future work
    • References
    • Chapter 6: Multiatlas segmentation
    • Abstract
    • Glossary
    • 6.1. Introduction
    • 6.2. History of atlas-based segmentation
    • 6.3. Mathematical framework
    • 6.4. Connection between multiatlas segmentation and machine learning
    • 6.5. Multiatlas segmentation using machine learning
    • 6.6. Machine learning using multiatlas segmentation
    • 6.7. Integrating multiatlas segmentation and machine learning
    • 6.8. Challenges and applications
    • 6.9. Unsolved problems
    • References
    • Chapter 7: Segmentation using adversarial image-to-image networks
    • Abstract
    • 7.1. Introduction
    • 7.2. Segmentation using an adversarial image-to-image network
    • 7.3. Volumetric domain adaptation with intrinsic semantic cycle consistency
    • References
    • Chapter 8: Multimodal medical volumes translation and segmentation with generative adversarial network
    • Abstract
    • 8.1. Introduction
    • 8.2. Literature review
    • 8.3. Preliminary
    • 8.4. Method
    • 8.5. Network architecture and training details
    • 8.6. Experimental results
    • 8.7. Conclusions
    • References
    • Chapter 9: Landmark detection and multiorgan segmentation: Representations and supervised approaches
    • Abstract
    • 9.1. Introduction
    • 9.2. Landmark detection
    • 9.3. Multiorgan segmentation
    • 9.4. Conclusion
    • References
    • Chapter 10: Deep multilevel contextual networks for biomedical image segmentation
    • Abstract
    • Acknowledgement
    • 10.1. Introduction
    • 10.2. Related work
    • 10.3. Method
    • 10.4. Experiments and results
    • 10.5. Discussion and conclusion
    • References
    • Chapter 11: LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction
    • Abstract
    • Acknowledgements
    • 11.1. Introduction
    • 11.2. LOGISMOS
    • 11.3. Just-enough interaction
    • 11.4. Retinal OCT segmentation
    • 11.5. Coronary OCT segmentation
    • 11.6. Knee MR segmentation
    • 11.7. Modular application design
    • 11.8. Conclusion
    • References
    • Chapter 12: Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
    • Abstract
    • 12.1. Introduction
    • 12.2. Deep learning based segmentation of ventricles
    • 12.3. Shape refinement by sparse shape composition
    • 12.4. 3D modeling
    • 12.5. Conclusion and future directions
    • References
    • Chapter 13: Image registration with sliding motion
    • Abstract
    • 13.1. Challenges of motion discontinuities in medical imaging
    • 13.2. Sliding preserving regularization for Demons
    • 13.3. Discrete optimization for displacements
    • 13.4. Image registration for cancer applications
    • 13.5. Conclusions
    • References
    • Chapter 14: Image registration using machine and deep learning
    • Abstract
    • 14.1. Introduction
    • 14.2. Machine-learning-based registration
    • 14.3. Machine-learning-based multimodal registration
    • 14.4. Deep-learning-based registration
    • References
    • Chapter 15: Imaging biomarkers in Alzheimer's disease
    • Abstract
    • 15.1. Introduction
    • 15.2. Range of imaging modalities and associated biomarkers
    • 15.3. Biomarker extraction evolution
    • 15.4. Biomarkers in practice
    • 15.5. Biomarkers' strategies: practical examples
    • 15.6. Future avenues of image analysis for biomarkers in Alzheimer's disease
    • References
    • Chapter 16: Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
    • Abstract
    • 16.1. Introduction
    • 16.2. Large scale population studies in neuroimage analysis: steps towards dimensional neuroimaging; harmonization challenges
    • 16.3. Unsupervised pattern learning for dimensionality reduction of neuroimaging data
    • 16.4. Supervised classification based imaging biomarkers for disease diagnosis
    • 16.5. Multivariate pattern regression for brain age prediction
    • 16.6. Deep learning in neuroimaging analysis
    • 16.7. Revealing heterogeneity of imaging patterns of brain diseases
    • 16.8. Conclusions
    • References
    • Chapter 17: Imaging biomarkers for cardiovascular diseases
    • Abstract
    • 17.1. Introduction
    • 17.2. Cardiac imaging
    • 17.3. Cardiac shape and function
    • 17.4. Cardiac motion
    • 17.5. Coronary and vascular function
    • 17.6. Myocardial structure
    • 17.7. Population-based cardiac image biomarkers
    • References
    • Chapter 18: Radiomics
    • Abstract
    • Acknowledgement
    • 18.1. Introduction
    • 18.2. Data acquisition & preparation
    • 18.3. Segmentation
    • 18.4. Features
    • 18.5. Data mining
    • 18.6. Study design
    • 18.7. Infrastructure
    • 18.8. Conclusion
    • References
    • Chapter 19: Random forests in medical image computing
    • Abstract
    • 19.1. A different way to use context
    • 19.2. Feature selection and ensembling
    • 19.3. Algorithm basics
    • 19.4. Applications
    • 19.5. Conclusions
    • References
    • Chapter 20: Convolutional neural networks
    • Abstract
    • 20.1. Introduction
    • 20.2. Neural networks
    • 20.3. Convolutional neural networks
    • 20.4. CNN architectures for classification
    • 20.5. Practical methodology
    • 20.6. Future challenges
    • References
    • Chapter 21: Deep learning: RNNs and LSTM
    • Abstract
    • 21.1. From feedforward to recurrent
    • 21.2. Modeling with RNNs
    • 21.3. Training RNNs (and why simple RNNs aren't enough)
    • 21.4. Long short-term memory and gated recurrent units
    • 21.5. Example applications of RNNs at MICCAI
    • References
    • Chapter 22: Deep multiple instance learning for digital histopathology
    • Abstract
    • 22.1. Multiple instance learning
    • 22.2. Deep multiple instance learning
    • 22.3. Methodology
    • 22.4. MIL approaches
    • 22.5. MIL pooling functions
    • 22.6. Application to histopathology
    • References
    • Chapter 23: Deep learning: Generative adversarial networks and adversarial methods
    • Abstract
    • 23.1. Introduction
    • 23.2. Generative adversarial networks
    • 23.3. Adversarial methods for image domain translation
    • 23.4. Domain adaptation via adversarial training
    • 23.5. Applications in biomedical image analysis
    • 23.6. Discussion and conclusion
    • References
    • Chapter 24: Linear statistical shape models and landmark location
    • Abstract
    • 24.1. Introduction
    • 24.2. Shape models
    • 24.3. Automated landmark location strategies
    • 24.4. Discussion
    • Appendix 24.A.
    • References
    • Chapter 25: Computer-integrated interventional medicine: A 30 year perspective
    • Abstract
    • 25.1. Introduction: a three-way partnership between humans, technology, and information to improve patient care
    • 25.2. The information flow in computer-integrated interventional medicine
    • 25.3. Intraoperative systems for CIIM
    • 25.4. Emerging research themes
    • References
    • Chapter 26: Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT
    • Abstract
    • 26.1. The 2D imaging chain
    • 26.2. The 3D imaging chain
    • 26.3. System embodiments
    • 26.4. Applications
    • References
    • Chapter 27: Interventional imaging: MR
    • Abstract
    • 27.1. Motivation
    • 27.2. Technical background
    • 27.3. Clinical applications
    • References
    • Chapter 28: Interventional imaging: Ultrasound
    • Abstract
    • Acknowledgements
    • 28.1. Introduction: ultrasound imaging
    • 28.2. Ultrasound-guided cardiac interventions
    • 28.3. Ultrasound data manipulation and image fusion for cardiac applications
    • 28.4. Ultrasound imaging in orthopedics
    • 28.5. Image-guided therapeutic applications
    • 28.6. Summary and future perspectives
    • References
    • Chapter 29: Interventional imaging: Vision
    • Abstract
    • Acknowledgements
    • 29.1. Vision-based interventional imaging modalities
    • 29.2. Geometric scene analysis
    • 29.3. Visual scene interpretation
    • 29.4. Clinical applications
    • 29.5. Discussion
    • References
    • Chapter 30: Interventional imaging: Biophotonics
    • Abstract
    • 30.1. A brief introduction to light–tissue interactions and white light imaging
    • 30.2. Summary of chapter structure
    • 30.3. Fluorescence imaging
    • 30.4. Multispectral imaging
    • 30.5. Microscopy techniques
    • 30.6. Optical coherence tomography
    • 30.7. Photoacoustic methods
    • 30.8. Optical perfusion imaging
    • 30.9. Macroscopic scanning of optical systems and visualization
    • 30.10. Summary
    • References
    • Chapter 31: External tracking devices and tracked tool calibration
    • Abstract
    • 31.1. Introduction
    • 31.2. Target registration error estimation for paired measurements
    • 31.3. External spatial measurement devices
    • 31.4. Stylus calibration
    • 31.5. Template-based calibration
    • 31.6. Ultrasound probe calibration
    • 31.7. Camera hand–eye calibration
    • 31.8. Conclusion and resources
    • References
    • Chapter 32: Image-based surgery planning
    • Abstract
    • 32.1. Background and motivation
    • 32.2. General concepts
    • 32.3. Treatment planning for bone fracture in orthopaedic surgery
    • 32.4. Treatment planning for keyhole neurosurgery and percutaneous ablation
    • 32.5. Future challenges
    • References
    • Chapter 33: Human–machine interfaces for medical imaging and clinical interventions
    • Abstract
    • 33.1. HCI for medical imaging vs clinical interventions
    • 33.2. Human–computer interfaces: design and evaluation
    • 33.3. What is an interface?
    • 33.4. Human outputs are computer inputs
    • 33.5. Position inputs (free-space pointing and navigation interactions)
    • 33.6. Direct manipulation vs proxy-based interactions (cursors)
    • 33.7. Control of viewpoint
    • 33.8. Selection (object-based interactions)
    • 33.9. Quantification (object-based position setting)
    • 33.10. User interactions: selection vs position, object-based vs free-space
    • 33.11. Text inputs (strings encoded/parsed as formal and informal language)
    • 33.12. Language-based control (text commands or spoken language)
    • 33.13. Image-based and workspace-based interactions: movement and selection events
    • 33.14. Task representations for image-based and intervention-based interfaces
    • 33.15. Design and evaluation guidelines for human–computer interfaces: human inputs are computer outputs – the system design must respect perceptual capacities and constraints
    • 33.16. Objective evaluation of performance on a task mediated by an interface
    • References
    • Chapter 34: Robotic interventions
    • Abstract
    • 34.1. Introduction
    • 34.2. Precision positioning
    • 34.3. Master–slave system
    • 34.4. Image guided robotic tool guide
    • 34.5. Interactive manipulation
    • 34.6. Articulated access
    • 34.7. Untethered microrobots
    • 34.8. Soft robotics
    • 34.9. Summary
    • References
    • Chapter 35: System integration
    • Abstract
    • 35.1. Introduction
    • 35.2. System design
    • 35.3. Frameworks and middleware
    • 35.4. Development process
    • 35.5. Example integrated systems
    • 35.6. Conclusions
    • References
    • Chapter 36: Clinical translation
    • Abstract
    • 36.1. Introduction
    • 36.2. Definitions
    • 36.3. Useful researcher characteristics for clinical translation
    • 36.4. Example of clinical translation: 3D ultrasound-guided prostate biopsy
    • 36.5. Conclusions
    • References
    • Chapter 37: Interventional procedures training
    • Abstract
    • 37.1. Introduction
    • 37.2. Assessment
    • 37.3. Feedback
    • 37.4. Simulated environments
    • 37.5. Shared resources
    • 37.6. Summary
    • References
    • Chapter 38: Surgical data science
    • Abstract
    • Acknowledgements
    • 38.1. Concept of surgical data science (SDS)
    • 38.2. Clinical context for SDS and its applications
    • 38.3. Technical approaches for SDS
    • 38.4. Future challenges for SDS
    • 38.5. Conclusion
    • References
    • Chapter 39: Computational biomechanics for medical image analysis
    • Abstract
    • Acknowledgements
    • 39.1. Introduction
    • 39.2. Image analysis informs biomechanics: patient-specific computational biomechanics model from medical images
    • 39.3. Biomechanics informs image analysis: computational biomechanics model as image registration tool
    • 39.4. Discussion
    • References
    • Chapter 40: Challenges in Computer Assisted Interventions
    • Abstract
    • 40.1. Introduction to computer assisted interventions
    • 40.2. Advanced technology in computer assisted interventions
    • 40.3. Translational challenge
    • 40.4. Simulation
    • 40.5. Summary
    • References
    • Index

    Product details

    • No. of pages: 1072
    • Language: English
    • Copyright: © Academic Press 2019
    • Published: October 18, 2019
    • Imprint: Academic Press
    • eBook ISBN: 9780128165867
    • Hardcover ISBN: 9780128161760

    About the Editors

    Kevin Zhou

    Professor S. Kevin Zhou obtained his PhD degree from University of Maryland, College Park. He is a Professor at Chinese Academy of Sciences. Prior to this, he was a Principal Expert and a Senior R&D director at Siemens Healthcare. Dr. Zhou has published 180+ book chapters and peer-reviewed journal and conference papers, registered 250+ patents and inventions, written two research monographs, and edited three books. His two most recent books are entitled "Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches, SK Zhou (Ed.)" and "Deep Learning for Medical Image Analysis, SK Zhou, H Greenspan, DG Shen (Eds.)." He has won multiple awards including R&D 100 Award (Oscar of Invention), Siemens Inventor of the Year, and UMD ECE Distinguished Aluminum Award. He has been an associate editor for IEEE Transactions on Medical Imaging and Medical Image Analysis, an area chair for CVPR and MICCAI, a board member of the MICCAI Society. Professor Zhou is a Fellow of AIMBE.

    Affiliations and Expertise

    Professor, Institute of Computing Technology, Chinese Academy of Sciences, China

    Daniel Rueckert

    Professor Daniel Rueckert is Head of the Department of Computing at Imperial College London. He joined the Department of Computing as a lecturer in 1999 and became senior lecturer in 2003. Since 2005 he is Professor of Visual Information Processing. He has founded and leads the Biomedical Image Analysis group. His research interests include: Development of algorithms for image acquisition, image analysis and image interpretation, in particular in the areas of reconstruction, registration, tracking, segmentation and modelling; and novel machine learning approaches for the extraction of clinically useful information from medical images with application to computer-aided detection and diagnosis, computer-aided treatment planning, computer-guided interventions and therapy. He is an associate editor of IEEE Transactions on Medical Imaging, a member of the editorial board of Medical Image Analysis, Image & Vision Computing, MICCAI/Elsevier Book Series, and a referee for a number of international medical imaging journals and conferences. He has served as a member of organizing and program committees at numerous conferences, e.g. general co-chair of MMBIA 2006 and FIMH 2013 as well as program co-chair of MICCAI 2009, ISBI 2012 and WBIR 2012. He was elected as a Fellow of MICCAI in 2014, Fellow of the Royal Academy of Engineering in 2015 and, most recently, a Fellow of the Academy of Medical Sciences in 2019.

    Affiliations and Expertise

    Professor of Visual Information Processing and Head, Department of Computing, Imperial College London

    Gabor Fichtinger

    Professor Gabor Fichtinger is a Canada Research Chair in Computer-Integrated Surgery, at the School of Computing, Queen’s University, Canada. His research and teaching interests are Computer-Assisted Interventions, involving medical imaging, medical image analysis, visualization, surgical planning and navigation, robotics, biosensors, and integrating these component technologies into workable clinical systems. He further specializes in minimally invasive percutaneous (through the skin) interventions performed under image guidance, with primary application in the detection and treatment of cancer. He is an associate editor of IEEE Transactions on Biomedical Engineering, a member of the editorial board of Medical Image Analysis, and a deputy editor for the International Journal of Computer-Assisted Radiology and Surgery. He has served on the program and organizing committees of leading international conferences, including SPIE Medical Imaging and IPCAI; he was general co-chair for MICCAI 2011, and program co-chair for MICCAI 2008 and 2018. Professor Fichtinger is a Fellow of IEEE and a Fellow of MICCAI.

    Affiliations and Expertise

    Professor and Canada Research Chair in Computer-Integrated Surgery, School of Computing, Queen’s University, Ontario, Canada

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