Machine Learning and Medical Imaging

1st Edition - August 9, 2016
Editors: Guorong Wu, Dinggang Shen, Mert Sabuncu
Language: English
Hardback ISBN:
9 7 8 - 0 - 1 2 - 8 0 4 0 7 6 - 8
eBook ISBN:
9 7 8 - 0 - 1 2 - 8 0 4 1 1 4 - 7

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithm… Read more

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Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs.

The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians.

Editor Biographies
Preface
Acknowledgments
Part 1: Cutting-Edge Machine Learning Techniques in Medical Imaging
- Chapter 1: Functional connectivity parcellation of the human brain
  - Abstract
  - 1.1 Introduction
  - 1.2 Approaches to Connectivity-Based Brain Parcellation
  - 1.3 Mixture Model
  - 1.4 Markov Random Field Model
  - 1.5 Summary
- Chapter 2: Kernel machine regression in neuroimaging genetics
  - Abstract
  - Acknowledgments
  - 2.1 Introduction
  - 2.2 Mathematical Foundations
  - 2.3 Applications
  - 2.4 Conclusion and Future Directions
  - Appendix A Reproducing Kernel Hilbert Spaces
  - Appendix B Restricted Maximum Likelihood Estimation
- Chapter 3: Deep learning of brain images and its application to multiple sclerosis
  - Abstract
  - Acknowledgments
  - 3.1 Introduction
  - 3.2 Overview of Deep Learning in Neuroimaging
  - 3.3 Focus on Deep Learning in Multiple Sclerosis
  - 3.4 Future Research Needs
- Chapter 4: Machine learning and its application in microscopic image analysis
  - Abstract
  - 4.1 Introduction
  - 4.2 Detection
  - 4.3 Segmentation
  - 4.4 Summary
- Chapter 5: Sparse models for imaging genetics
  - Abstract
  - 5.1 Introduction
  - 5.2 Basic Sparse Models
  - 5.3 Structured Sparse Models
  - 5.4 Optimization Methods
  - 5.5 Screening
  - 5.6 Conclusions
- Chapter 6: Dictionary learning for medical image denoising, reconstruction, and segmentation
  - Abstract
  - 6.1 Introduction
  - 6.2 Sparse Coding and Dictionary Learning
  - 6.3 Patch-Based Dictionary Sparse Coding
  - 6.4 Application of Dictionary Learning in Medical Imaging
  - 6.5 Future Directions
  - 6.6 Conclusion
  - Glossary
- Chapter 7: Advanced sparsity techniques in magnetic resonance imaging
  - Abstract
  - 7.1 Introduction
  - 7.2 Standard Sparsity in CS-MRI
  - 7.3 Group Sparsity in Multicontrast MRI
  - 7.4 Tree Sparsity in Accelerated MRI
  - 7.5 Forest Sparsity in Multichannel CS-MRI
  - 7.6 Conclusion
- Chapter 8: Hashing-based large-scale medical image retrieval for computer-aided diagnosis
  - Abstract
  - 8.1 Introduction
  - 8.2 Related Work
  - 8.3 Supervised Hashing for Large-Scale Retrieval
  - 8.4 Results
  - 8.5 Discussion and Future Work
Part 2: Successful Applications in Medical Imaging
- Chapter 9: Multitemplate-based multiview learning for Alzheimer’s disease diagnosis
  - Abstract
  - 9.1 Background
  - 9.2 Multiview Feature Representation With MR Imaging
  - 9.3 Multiview Learning Methods for AD Diagnosis
  - 9.4 Experiments
  - 9.5 Summary
- Chapter 10: Machine learning as a means toward precision diagnostics and prognostics
  - Abstract
  - 10.1 Introduction
  - 10.2 Dimensionality Reduction
  - 10.3 Model Interpretation: From Classification to Statistical Significance Maps
  - 10.4 Heterogeneity
  - 10.5 Applications
  - 10.6 Conclusion
- Chapter 11: Learning and predicting respiratory motion from 4D CT lung images
  - Abstract
  - Acknowledgment
  - 11.1 Introduction
  - 11.2 3D/4D CT Lung Image Processing
  - 11.3 Extracting and Estimating Motion Patterns From 4D CT
  - 11.4 An Example for Image-Guided Intervention
  - 11.5 Concluding Remarks
- Chapter 12: Learning pathological deviations from a normal pattern of myocardial motion: Added value for CRT studies?
  - Abstract
  - Acknowledgments
  - 12.1 Introduction
  - 12.2 Features Extraction: Statistical Distance from Normal Motion
  - 12.3 Manifold Learning: Characterizing Pathological Deviations from Normality
  - 12.4 Back to the Clinical Application: Understanding CRT-Induced Changes
  - 12.5 Discussion/Future Work
- Chapter 13: From point to surface: Hierarchical parsing of human anatomy in medical images using machine learning technologies
  - Abstract
  - 13.1 Introduction
  - 13.2 Literature Review
  - 13.3 Anatomy Landmark Detection
  - 13.4 Detection of Anatomical Boxes
  - 13.5 Coarse Organ Segmentation
  - 13.6 Precise Organ Segmentation
  - 13.7 Conclusion
- Chapter 14: Machine learning in brain imaging genomics
  - Abstract
  - 14.1 Introduction
  - 14.2 Mining Imaging Genomic Associations Via Regression or Correlation Analysis
  - 14.3 Mining Higher Level Imaging Genomic Associations Via Set-Based Analysis
  - 14.4 Discussion
- Chapter 15: Holistic atlases of functional networks and interactions (HAFNI)
  - Abstract
  - Acknowledgments
  - 15.1 Introduction
  - 15.2 HAFNI for Functional Brain Network Identification
  - 15.3 HAFNI Applications
  - 15.4 HAFNI-Based New Methods
  - 15.5 Future Directions of HAFNI Applications
- Chapter 16: Neuronal network architecture and temporal lobe epilepsy: A connectome-based and machine learning study
  - Abstract
  - 16.1 Introduction
  - 16.2 Treatment Outcome Prediction of Patients With TLE
  - 16.3 Naming Impairment Performance of Patients With TLE
Index

Guorong Wu

Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu’s research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition. He has published more than 100 papers in the international journals and conferences. Dr. Wu is actively in the development of medical image processing software to facilitate the scientific research on neuroscience and radiology therapy.

Affiliations and expertise

Assistant Professor of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, USA

Dinggang Shen

Dinggang Shen, PhD is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai. He is a Fellow of IEEE, AIMBE, IAPR and MICCAI. He was a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with the University of North Carolina at Chapel Hill (UNC-CH), Chapel Hill, NC, USA. His research interests include medical image analysis, computer vision and pattern recognition. He has published more than 1,500 peer-reviewed papers in the international journals and conference proceedings, with H-index 130 and over 70K citations.

Affiliations and expertise

Professor, Department of Radiology and BRIC, UNC-Chapel Hill, USA

Mert Sabuncu

Mert Sabuncu is an Assistant Professor in Electrical and Computer Engineering, with a secondary appointment in Biomedical Engineering, Cornell University. His research interests are in biomedical data analysis, in particular imaging data, and with an application emphasis on neuroscience and neurology. He uses tools from signal/image processing, probabilistic modeling, statistical inference, computer vision, computational geometry, graph theory, and machine learning to develop algorithms that allow learning from large-scale biomedical data.

Affiliations and expertise

Assistant Professor, Electrical and Computer Engineering, Secondary Appointment in Biomedical Engineering, Cornell University

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Machine Learning and Medical Imaging

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Guorong Wu

Dinggang Shen

Mert Sabuncu