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Handbook of Medical Image Computing and Computer Assisted Intervention

  • 1st Edition - October 18, 2019
  • Latest edition
  • Editors: S. Kevin Zhou, Daniel Rueckert, Gabor Fichtinger
  • Language: English

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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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

    1. Image synthesis and superresolution in medical imaging
    Jerry L. Prince, Aaron Carass, Can Zhao, Blake E. Dewey, Snehashis Roy, Dzung L. Pham

    2. Machine learning for image reconstruction
    Kerstin Hammernik, Florian Knoll

    3. Liver lesion detection in CT using deep learning techniques
    Avi Ben-Cohen, Hayit Greenspan

    4. CAD in lung
    Kensaku Mori

    5. Text mining and deep learning for disease classification
    Yifan Peng, Zizhao Zhang, Xiaosong Wang, Lin Yang, Le Lu

    6. Multiatlas segmentation
    Bennett A. Landman, Ilwoo Lyu, Yuankai Huo, Andrew J. Asman

    7. Segmentation using adversarial image-to-image networks
    Dong Yang, Tao Xiong, Daguang Xu, S. Kevin Zhou

    8. Multimodal medical volumes translation and segmentation with generative adversarial network
    Zizhao Zhang, Lin Yang, Yefeng Zheng

    9. Landmark detection and multiorgan segmentation: Representations and supervised approaches
    S. Kevin Zhou, Zhoubing Xu

    10. Deep multilevel contextual networks for biomedical image segmentation
    Hao Chen, Qi Dou, Xiaojuan Qi, Jie-Zhi Cheng, Pheng-Ann Heng

    11. LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction
    Honghai Zhang, Kyungmoo Lee, Zhi Chen, Satyananda Kashyap, Milan Sonka

    12. Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics
    Dimitris N. Metaxas, Zhennan Yan

    13. Image registration with sliding motion
    Mattias P. Heinrich, Bartłomiej W. Papiez˙

    14. Image registration using machine and deep learning
    Xiaohuan Cao, Jingfan Fan, Pei Dong, Sahar Ahmad, Pew-Thian Yap, Dinggang Shen

    15. Imaging biomarkers in Alzheimer’s disease
    Carole H. Sudre, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin

    16. Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective
    Guray Erus, Mohamad Habes, Christos Davatzikos

    17. Imaging biomarkers for cardiovascular diseases
    Avan Suinesiaputra, Kathleen Gilbert, Beau Pontre, Alistair A. Young

    18. Radiomics
    Martijn P.A. Starmans, Sebastian R. van der Voort, Jose M. Castillo Tovar, Jifke F. Veenland, Stefan Klein, Wiro J. Niessen

    19. Random forests in medical image computing
    Ender Konukoglu, Ben Glocker

    20. Convolutional neural networks
    Jonas Teuwen, Nikita Moriakov

    21. Deep learning: RNNs and LSTM
    Robert DiPietro, Gregory D. Hager

    22. Deep multiple instance learning for digital histopathology
    Maximilian Ilse, Jakub M. Tomczak, Max Welling

    23. Deep learning: Generative adversarial networks and adversarial methods
    Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum

    24. Linear statistical shape models and landmark location
    T.F. Cootes

    25. Computer-integrated interventional medicine: A 30 year perspective
    Russell H. Taylor

    26. Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT
    Sebastian Schafer, Jeffrey H. Siewerdsen

    27. Interventional imaging: MR
    Eva Rothgang, William S. Anderson, Elodie Breton, Afshin Gangi, Julien Garnon, Bennet Hensen, Brendan F. Judy, Urte Kägebein, Frank K. Wacker

    28. Interventional imaging: Ultrasound
    Ilker Hacihaliloglu, Elvis C.S. Chen, Parvin Mousavi, Purang Abolmaesumi, Emad Boctor, Cristian A. Linte

    29. Interventional imaging: Vision
    Stefanie Speidel, Sebastian Bodenstedt, Francisco Vasconcelos, Danail Stoyanov

    30. Interventional imaging: Biophotonics
    Daniel S. Elson

    31. External tracking devices and tracked tool calibration
    Elvis C.S. Chen, Andras Lasso, Gabor Fichtinger

    32. Image-based surgery planning
    Caroline Essert, Leo Joskowicz

    33. Human–machine interfaces for medical imaging and clinical interventions
    Roy Eagleson, Sandrine de Ribaupierre

    34. Robotic interventions
    Sang-Eun Song

    35. System integration
    Andras Lasso, Peter Kazanzides

    36. Clinical translation
    Aaron Fenster

    37. Interventional procedures training
    Tamas Ungi, Matthew Holden, Boris Zevin, Gabor Fichtinger

    38. Surgical data science
    Gregory D. Hager, Lena Maier-Hein, S. Swaroop Vedula

    39. Computational biomechanics for medical image analysis
    Adam Wittek, Karol Miller

    40. Challenges in Computer Assisted Interventions
    P. Stefan, J. Traub, C. Hennersperger, M. Esposito, N. Navab　

    Product details

    • Edition: 1
    • Latest edition
    • Published: October 18, 2019
    • Language: English

    About the editors

    SZ

    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

    DR

    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

    GF

    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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