Handbook of Medical Image Computing and Computer Assisted Intervention

著者名：Zhou, S. Kevin (EDT)/Rueckert, Daniel (EDT)/Fichtinger, Gabor (EDT)

Academic Press（2019/10/18発売）

• 言語：ENG
• ISBN：9780128161760
• eISBN：9780128165867

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.- 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

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. Pham2. Machine learning for image reconstructionKerstin Hammernik, Florian Knoll3. Liver lesion detection in CT using deep learning techniques Avi Ben-Cohen, Hayit Greenspan4. CAD in lungKensaku Mori5. Text mining and deep learning for disease classificationYifan Peng, Zizhao Zhang, Xiaosong Wang, Lin Yang, Le Lu6. Multiatlas segmentationBennett A. Landman, Ilwoo Lyu, Yuankai Huo, Andrew J. Asman7. Segmentation using adversarial image-to-image networks Dong Yang, Tao Xiong, Daguang Xu, S. Kevin Zhou8. Multimodal medical volumes translation and segmentation with generative adversarial network Zizhao Zhang, Lin Yang, Yefeng Zheng9. Landmark detection and multiorgan segmentation: Representations and supervised approaches S. Kevin Zhou, Zhoubing Xu10. Deep multilevel contextual networks for biomedical image segmentation Hao Chen, Qi Dou, Xiaojuan Qi, Jie-Zhi Cheng, Pheng-Ann Heng11. LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction Honghai Zhang, Kyungmoo Lee, Zhi Chen, Satyananda Kashyap, Milan Sonka12. Deformable models, sparsity and learning-based segmentation for cardiac MRI based analyticsDimitris N. Metaxas, Zhennan Yan13. 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 Shen15. Imaging biomarkers in Alzheimer's disease Carole H. Sudre, M. Jorge Cardoso, Marc Modat, Sebastien Ourselin16. Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective Guray Erus, Mohamad Habes, Christos Davatzikos17. Imaging biomarkers for cardiovascular diseases Avan Suinesiaputra, Kathleen Gilbert, Beau Pontre, Alistair A. Young18. Radiomics Martijn P.A. Starmans, Sebastian R. van der Voort, Jose M. Castillo Tovar, Jifke F. Veenland, Stefan Klein, Wiro J. Niessen19. Random forests in medical image computing Ender Konukoglu, Ben Glocker20. Convolutional neural networks Jonas Teuwen, Nikita Moriakov21. Deep learning: RNNs and LSTM Robert DiPietro, Gregory D. Hager22. Deep multiple instance learning for digital histopathology Maximilian Ilse, Jakub M. Tomczak, Max Welling23. Deep learning: Generative adversarial networks and adversarial methods Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig, Ivana Išgum24. Linear statistical shape models and landmark location T.F. Cootes25. Computer-integrated interventional medicine: A 30 year perspective Russell H. Taylor26. Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CTSebastian Schafer, Jeffrey H. Siewerdsen27. Interventional imaging: MR Eva Rothgang, William S. Anderson, Elodie Breton, Afshin Gangi, Julien Garnon, Bennet Hensen, Brendan F. Judy, Urte Kägebein, Frank K. Wacker28. Interventional imaging: Ultrasound Ilker Hacihaliloglu, Elvis C.S. Chen, Parvin Mousavi, Purang Abolmaesumi, Emad Boctor, Cristian A. Linte29. Interventional imaging: Vision Stefanie Speidel, Sebastian Bodenstedt, Francisco Vasconcelos, Danail Stoyanov30. Interventional imaging: Biophotonics Daniel S. Elson31. External tracking devices and tracked tool calibration Elvis C.S. Chen, Andras Lasso, Gabor Fichtinger32. Image-based surgery planning Caroline Essert, Leo Joskowicz33. Human–machine interfaces for medical imaging and clinical interventions Roy Eagleson, Sandrine de Ribaupierre34. Robotic interventions Sang-Eun Song35. System integration Andras Lasso, Peter Kazanzides36. Clinical translation Aaron Fenster37. Interventional procedures trainingTamas Ungi, Matthew Holden, Boris Zevin, Gabor Fichtinger38. Surgical data science Gregory D. Hager, Lena Maier-Hein, S. Swaroop Vedula39. Computational biomechanics for medical image analysis Adam Wittek, Karol Miller40.