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. In 2014, he has been elected as a Fellow of the MICCAI society and in 2015 he was elected as a Fellow of the Royal Academy of Engineering.
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et al., 2013, Automated Abdominal Multi-Organ Segmentation With Subject-Specific Atlas Generation, IEEE Transactions on Medical Imaging, Vol:32, ISSN:0278-0062, Pages:1723-1730
et al., 2013, Temporal sparse free-form deformations, Medical Image Analysis, Vol:17, ISSN:1361-8415, Pages:779-789
et al., 2013, Random forest-based similarity measures for multi-modal classification of Alzheimer's disease, Neuroimage, Vol:65, ISSN:1053-8119, Pages:167-175
et al., 2011, A Combined Manifold Learning Analysis of Shape and Appearance to Characterize Neonatal Brain Development, IEEE Transactions on Medical Imaging, Vol:30, ISSN:0278-0062, Pages:2072-2086
et al., 2010, Fast and robust multi-atlas segmentation of brain magnetic resonance images, Neuroimage, Vol:49, ISSN:1053-8119, Pages:2352-2365
et al., 2010, LEAP: Learning embeddings for atlas propagation, Neuroimage, Vol:49, ISSN:1053-8119, Pages:1316-1325