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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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


    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

    • 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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