I'm interested in the development and application of imaging science in medicine and biology, and the development of imaging science as a coherent discipline for trainees.

For almost 40 years my research has spanned different areas of imaging science - from basic physics of image formation, to understanding the factors that affect the signals used to make images, to applications that derive new types of information about biological systems, to image analysis and perception. Imaging of human subjects, non-human primates and small animals provides unique information on tissue structure and function, and is being applied in a variety of different applications in neuroscience, cancer research and studies of metabolism. A general theme of interest is to understand the physical and physiological factors that affect MRI signals and to use this knowledge to devise non-invasive imaging methods that provide new types of information, as well as for developing new applications of imaging. Fundamental studies of the basis of MRI contrast mechanisms such as proton relaxation, diffusion, and the BOLD effect in fMRI help interpret images used in numerous applications. A second major theme is the development of methods for studying human brain structure and function using MRI, especially at very high fields, and for integrating fMRI data with other imaging methods such as NIR and EEG. Several novel engineering solutions are being developed to address the technical challenges of human imaging at 7 Tesla, while other research to develop robust measurements of neurotransmitters and metabolism by multinuclear MRS is also being pursued. Applications of structural and functional MRI to the brain are performed in collaboration with investigators from psychology, psychiatry and other departments. A third major theme is the use of multi-modality imaging (MRI, PET, CT, optical and ultrasound) to study small animals, especially mouse models of human cancer and other genetically modified mice. New technologies being developed include acousto-optical imaging of fluorophores. Many projects also involve the development and application of advanced image analysis methods and computer algorithms.

Wiro Niessen 博士

荷兰皇家艺术与科学院院士

鹿特丹Erasmus医学中心教授

荷兰代尔夫特理工大学教授

MICCAI学会主席、会士

MICCAI大会主席

IEEE TMI期刊Associate Editor

研究方向：计算机视觉、生物医学图像分析和计算机辅助干预

主页：http://www.bigr.nl/people/WiroNiessen/

Wiro Niessen was born in Geldrop, The Netherlands, on November 15, 1969. He received a MSc degree (cum laude) in Physics from Utrecht University, the Netherlands in 1993, and a PhD degree (cum laude) in Medical Imaging from Utrecht University, the Netherlands, in 1997. Part of the MSc programme was carried out at the University of Wisconsin, Madison, USA, and part of the PhD programme at Yale University, USA.

From 1997-2004 he was Postdoctoral Researcher, Assistant Professor and Associate Professor at the Image Science Institute of the University Medical Center Utrecht. In this period he was leading research theme groups in the fields of cardiovascular image analysis and image guided interventions, and supervised eleven PhD theses in these fields.

In 2005, he was appointed as a Full Professor of Biomedical Image Processing in the Departments of Radiology and Medical Informatics at the Erasmus MC. His research interests include many aspects of computer vision, biomedical image analysis, and computer assisted interventions. In 2005 he was also appointed Professor at Delft University of Technology, at the faculty of Applied Sciences.

Wiro Niessen was elected to the Dutch Young Academy (DJA) in 2005, and is Board Member of the Dutch Young Academy since 2009. He is Associate Editor of the IEEE Transactions on Medical Imaging and Elsevier’s Medical Image Analysis. He served as a Guest Editor on two Special Issues on Model-Based Image Analysis, and Growth and Motion Analysis of the IEEE Transactions on Medical Imaging, on a Special Issue on Medical Image Computing and Computer Assisted Intervention of Medical Image Analysis, and on a special issue on mathematical methods in biomedical image analysis in the International Journal of Computer Vision. He has (co)-authored over 230 publications, abstracts excluded, in the fields of computer vision, medical image analysis and image guided surgery, of which over 90 are in peer-reviewed international journals, and of which 5 received best paper awards.

Daniel Rueckert 博士

帝国理工学院教授

计算机系主任

皇家工程院院士

IEEE Fellow、MICCAI society Fellow

曾担任MMBIA、FIMH大会主席，MICCAI、ISBI、WBIR程序主席

IEEE Transactions on Medical Imaging副主编

医学影像分析、影像与视觉计算编辑委员会成员

研究方向：生物医学图像分析与计算、医学影像中的机器学习

主页：http://wp.doc.ic.ac.uk/dr/

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 and leads 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 and as fellow of the IEEE.

Nicholas Ayache 博士

法国INRIA研究主任

法国科学院院士

法国外科学院院士

《Medical Image Analysis》期刊主编

MICCAI学会会士

MICCAI、FIMH、IS4TM和CVRMed等会议大会主席

研究方向：生物医学图像分析和仿真

主页：http://www-sop.inria.fr/members/Nicholas.Ayache/

I am a Research Director at Inria , the French Research Institute of Computer Science and Applied Mathematics, where I lead the EPIONE project-team whose research activities are focusing on the Analysis and Simulation of Biomedical Images. My current research interests are in biomedical image analysis and simulation. This includes the analysis of medical and biological images with advanced geometrical, statistical, physical and functional models, the simulation of physiological systems with computational models built from biomedical images and other signals, and the application of these tools to medicine and biology to better assist prevention, diagnosis and therapy of diseases.

For past and current results please refer to my publications and citation index according to Google Scholar.

I am also serving on the board of several international journals and conferences.

In 2014, I won the Grand Prize Inria - Académie des sciences and was elected at the French Academy of sciences.

In 2011, I was awarded an ERC advanced grant for the analysis of dynamic medical images (MedYMA 2012-2017) 

In 2008, I received the Microsoft Award for Science in Europe, and in 2006 the EADS Grand Prize in Information Science.

In 2013, I was elected a professor at the Collège de France, on the Chair Informatics and Computational Sciences for the academic year 2013-2014. More information here.

In 2017, I was elected at the French Academy of Surgery as a free member, for my contributions to the "Personalized Digital Patient".