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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
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
- No. of pages:
- © Academic Press 2019
- 18th October 2019
- Academic Press
- eBook ISBN:
- Hardcover ISBN:
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, Institute of Computing Technology, Chinese Academy of Sciences
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 of Visual Information Processing and Head, Department of Computing, Imperial College London
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.
Professor and Canada Research Chair in Computer-Integrated Surgery, School of Computing, Queen’s University, Ontario, Canada
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