Handbook of Medical Image Computing and Computer Assisted Intervention
- 1st Edition - October 19, 2019
- Latest edition
- Editors: S. Kevin Zhou, Daniel Rueckert, Gabor Fichtinger
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 8 1 6 1 7 6 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 1 6 5 8 6 - 7
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
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
Researchers, graduate students and practitioners in medical imaging, computer assisted intervention, computer vision and biomedical engineering.
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
- Edition: 1
- Latest edition
- Published: October 19, 2019
- Language: English
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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, USADR
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 LondonGF
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, CanadaRead Handbook of Medical Image Computing and Computer Assisted Intervention on ScienceDirect