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Handbook of Medical Image Computing and Computer Assisted Intervention de S. Kevin Zhou

Handbook of Medical Image Computing and Computer Assisted Intervention: The MICCAI Society book Series

Editat de S. Kevin Zhou, Daniel Rueckert, Gabor Fichtinger
en Limba Engleză Hardback – 18 oct 2019
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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Specificații

ISBN-13: 9780128161760
ISBN-10: 0128161760
Pagini: 1072
Dimensiuni: 191 x 235 x 52 mm
Greutate: 2.03 kg
Editura: ELSEVIER SCIENCE
Seria The MICCAI Society book Series

Public țintă

Researchers, graduate students and practitioners in medical imaging, computer assisted intervention, computer vision and biomedical engineering.

Cuprins

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 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