Handbook of Medical Image Computing and Computer Assisted Intervention (inbunden)
Inbunden (Hardback)
Antal sidor
Academic Press
Rueckert, Daniel / Fichtinger, Gabor
Color illustrations
235 x 190 x 56 mm
2034 g
Antal komponenter
1370:Standard Color 7.5 x 9.25 in or 235 x 191 mm Case Laminate on White w/Gloss Lam
Handbook of Medical Image Computing and Computer Assisted Intervention (inbunden)

Handbook of Medical Image Computing and Computer Assisted Intervention

Inbunden Engelska, 2019-10-19
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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.

  • 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
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Fler böcker av S Kevin Zhou

Övrig information

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. 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. 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 applicat...


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, Bartlomiej 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 Isgum 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, Br...