Consensus between Pipelines in Structural Brain Networks, Christopher S. Parker, Fani Deligianni, M. Jorge Cardoso, Pankaj Daga, Marc Modat, Michael Dayan, Chris A. Clark, Sebastien Ourselin, Jonathan D. Clayden, PLOS ONE (Nov 2014) DOI: 10.1371/journal.pone.0111262

Paper accepted in PLOS ONE. Download it here

As of 1st November 2014, Marc Modat has been appointed Lecturer in Translational Neuroimaging. Marc will lead and grow an exciting research programme within the UCL Leonard Wolfson Experimental Neurology Centre with a particular emphasis on the development of robust imaging biomarkers for different neurological conditions.

Well done!

Brain volume estimation from post-mortem newborn and fetal MRI - Eliza Orasanu, Andrew Melbourne, Jorge M. Cardoso, Marc Modat, Andrew M. Taylor, Sudhin Thayyil, Sebastien Ourselin - Neuroimage: Clinical (October 2014). doi:10.106/j.nicl.2014.10.007

Paper accepted in Neuroimage: Clinical. Download it here 

ART EXHIBITION | Monday 13th - Sunday 19th October 2014 @ The Portico, UCL Main Quad, Gower Street, WC1E 6BT


This art exhibition supports our Wellcome Trust & Department of Health funded translational research project aiming at developping a clinical system for detecting anomalies in brain scans with the aid of machine-learning (HICF-R9-501).  

Measuring brain atrophy with a generalized formulation of the boundary shift integral - Ferran Prados, Manuel Jorge Cardoso, Kelvin K. Leung, David M. Cash, Marc Modat, Nick C. Fox, Claudia A.M. Wheeler-Kingshott, Sebastien Ourselin, for the Alzheimer's Disease Neuroimaging Initiative - Neurobiology of Aging 2014


New paper accepted as part of a Neurobiology of Ageing special issue. Download it here.


Global image registration using a symmetric block-matching approach - Marc Modat ; D. M. Cash ; P. Daga ; G. P. Winston ; J. S. Duncan ; S. Ourselin - J. Med. Imag. 1(2), 024003 (Sep 19, 2014). doi:10.1117/1.JMI.1.2.024003

You can access the publisher page here. The associated code can be find in the NiftyReg package.

A member of TIG is co-editing a Computer Vision and Image Understanding special issue on Probabilistic Models for Biomedical Image Analysis. This special issue aims at bridging the gap between researchers in computer vision, biomedical image analysis and machine learning by providing a platform for the exploration of probabilistic modeling approaches for difficult clinical problems within a variety of biomedical imaging context. For more information click here.


We are co-organising BAMBI'14. The workshop will take place in Boston as a satelite workshop of MICCAI 2014. The goal is to highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis. The program will consist of three keynote talks, from prominent figures in the community, and the presentation of previously unpublished and contributive papers. See more information here