Handbook of Medical Image Computing and Computer Assisted Intervention

1st Edition - October 18, 2019
Imprint: Academic Press
Editors: S. Kevin Zhou, Daniel Rueckert, Gabor Fichtinger
Language: English
Hardback ISBN:
9 7 8 - 0 - 1 2 - 8 1 6 1 7 6 - 0
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 1 6 5 8 6 - 7

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… Read more

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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.

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. Erratum
34.2. Introduction
34.3. Precision positioning
34.4. Master–slave system
34.5. Image guided robotic tool guide
34.6. Interactive manipulation
34.7. Articulated access
34.8. Untethered microrobots
34.9. Soft robotics
34.10. 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

S. Kevin Zhou

S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer..

Affiliations and expertise

Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA

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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Handbook of Medical Image Computing and Computer Assisted Intervention

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