Handbook of Medical Image Computing and Computer Assisted Intervention - 1st Edition - ISBN: 9780128161760, 9780128165867

Handbook of Medical Image Computing and Computer Assisted Intervention

1st Edition

Editors: S. Kevin Zhou Daniel Rueckert Gabor Fichtinger
Hardcover ISBN: 9780128161760
eBook ISBN: 9780128165867
Imprint: Academic Press
Published Date: 19th October 2019
Page Count: 1072
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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

    Medical Image Computing
    1. Image Synthesis and Super-resolution in Medical Imaging
    2. Machine Learning for Image Reconstruction
    3. Liver Lesion Detection in CT using Deep Learning Techniques
    4. Computer Aided Diagnosis in Lung
    5. Text Mining and Deep Learning for Disease Classification
    6. Multi-Atlas Segmentation
    7. Segmentation using Adversarial Image-to-image Networks
    8. Multimodal Medical Volumes Translation and Segmentation with Generative Adversarial Network
    9. Landmark Detection and Multi-organ Segmentation: Representations and Supervised Approaches
    10. Deep Multi-level 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 Disease
    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

    Computer Assisted Interventions
    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. Systems Integration
    36. Clinical Translation
    37. Interventional Procedures Training
    38. Surgical Data Science
    39. Computational Biomechanics for Medical Imaging
    40. Challenges in Computer Assisted Interventions 

    Details

    No. of pages:
    1072
    Language:
    English
    Copyright:
    © Academic Press 2020
    Published:
    19th October 2019
    Imprint:
    Academic Press
    Hardcover ISBN:
    9780128161760
    eBook ISBN:
    9780128165867

    About the Editor

    S. 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

    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

    Ratings and Reviews