Prof. Daniel Rueckert
Professor of Visual Information Processing
Daniel Rueckert 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 and heads the Biomedical Image Analysis group. He received a Diploma in Computer Science (equiv to M.Sc.) from the Technical University Berlin and a Ph.D. in Computer Science from Imperial College London. Before moving to Imperial College, he has worked as a post-doctoral research fellow in the Division of Radiological Sciences and Medical Engineering, King’s College London where he has worked on the development of non-rigid registration algorithms for the compensation of tissue motion and deformation. The developed registration techniques have been successfully used for the non-rigid registration of various anatomical structures, including in the breast, liver, heart and brain and are currently commercialized by IXICO, an Imperial College spin-out company. During his doctoral and post-doctoral research he has published more than 300 journal and conference articles. Professor Rueckert is an associate editor of IEEE Transactions on Medical Imaging, a member of the editorial board of Medical Image Analysis, Image & Vision Computing and a referee for a number of international medical imaging journals and conferences. He has served as a member of organising and programme committees at numerous conferences, e.g. he has been General Co-chair of MMBIA 2006 and FIMH 2013 as well as Programme Co-Chair of MICCAI 2009, ISBI 2012 and WBIR 2012.
- Konstantinos Kamnitsas, Christian Ledig, Virginia FJ Newcombe, Joanna P Simpson, Andrew D Kane, David K Menon, Daniel Rueckert, Ben Glocker, Efficient Multi-Scale 3D CNN with fully connected CRF for Accurate Brain Lesion Segmentation, arXiv preprint arXiv:1603.05959, 2016
- Amir Alansary, Matthew Lee, Kevin Keraudren, Bernhard Kainz, Christina Malamateniou, Mary A. Rutherford, Joseph V. Hajnal, Ben Glocker, Daniel Rueckert, Automatic Brain Localization in Fetal MRI Using Superpixel Graphs, ICML Workshop on Machine Learning meets Medical Imaging, volume 9487, pp.13–22, 2015
- Fahdi Kanavati, Tong Tong, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker, Supervoxel Classification Forests for Estimating Pairwise Image Correspondences, MICCAI, volume 9352, pp.94–101, 2015
- Veronika A. M. Zimmer, Ben Glocker, Paul Aljabar, Serena J. Counsell, Mary A. Rutherford, A. David Edwards, Joseph V. Hajnal, Miguel 'Angel Gonz'alez Ballester, Daniel Rueckert, Gemma Piella, Learning and Combining Image Similarities for Neonatal Brain Population Studies, MICCAI, volume 9352, pp.110–117, 2015
- Konstantinos Kamnitsas, Liang Chen, Christian Ledig, Daniel Rueckert, Ben Glocker, Multi-Scale 3D Convolutional Neural Networks for Lesion Segmentation in Brain MRI, MICCAI Brain Lesion Workshop, 2015