Handbook of Medical Image Computing and Computer Assisted Intervention

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.. 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 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.

• 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. Challenges in Computer Assisted Interventions P. Stefan, J. Traub, C. Hennersperger, M. Esposito, N. Navab