Description of the NiftySim FEM software package, its architecture and use cases. Stian F. Johnsen, Zeike A. Taylor, Matthew J. Clarkson, John Hipwell, Marc Modat, Bjoern Eiben, Lianghao Han, Yipeng Hu, Thomy Mertzanidou, David J. Hawkes, and Sebastien Ourselin. International Journal of Computer Assisted Radiology and Surgery. 2014. The article is available from here.
The NifTK software platform for image-guided interventions: platform overview and NiftyLink messaging, Matthew J. Clarkson, Gergely Zombori, Steve Thompson, Johannes Totz, Yi Song, Miklos Espak, Stian Johnsen, David Hawkes, Sebastien Ourselin, Int J Comput Assist Radiol Surg. 2014 Nov 20. PMID:25408304
The paper can be downloaded here.
In collaboration with the Institute of Nuclear Medicine (UCLH) and the Dementia Research Centre, TIG presented 5 papers at the IEEE NSS-MIC 2014 conference in Seattle. Among them, two were short-listed to participate in the student paper competitions.
The proceedings will soon be available on the IEEE Xplore website.
The purpose of this translational project is to demonstrate the feasibility, robustness, and clinical value of automated image analysis approaches on a large database of routine clinical brain imaging. This position specifically aims at developing a fully automated anomaly detection and triage system for clinical magnetic resonance brain imaging. The successful applicant will also bridge the gap between algorithm developers and end users, enhancing the translation to clinical practice. Additional duties will involve the integration of the developed system into the in-house NifTK software platform and the deployment of a scalable image analysis platform aiming at facilitating ‘big data’ approaches to major neurological disorders.
Researchers at UCL have been awarded a grant of £1m by the Department of Health and Wellcome Trust under the Health Innovation Challenge Fund initiative to tackle an obstacle to the continued growth of brain imaging by applying computer algorithms to detect anomalies in brain scans. Dr Parashkev Nachev and his collaborators Professors Geraint Rees, Professor Sebastien Ourselin, Professor Rolf Jager and Professor Xavier Golay are seeking to close this gap by applying novel computer-assisted algorithms so as to exploit much more of the information in each brain scan than a radiologist’s verbal report.
Right Ventricle Segmentation From Cardiac MRI: A Collation Study. Caroline Petitjean, Maria A. Zuluaga, Wenjia Bai, Jean-Nicolas Dacher, Damien Grosgeorge, Jérôme Caudron, Su Ruan, Ismail Ben Ayed, M. Jorge Cardoso, Hsiang-Chou Chen, Daniel Jimenez-Carretero, Maria J. Ledesma-Carbayo, Christos Davatzikos, Jimit Doshi, Guray Erus, Oskar M.O. Maier, Cyrus M.S. Nambakhsh, Yangming Ou, Sébastien Ourselin, Chun-Wei Peng, Nicholas S. Peters, Terry M. Peters, Martin Rajchl, Daniel Rueckert, Andres Santos, Wenzhe Shi, Ching-Wei Wang, Haiyan Wang, Jing Yuan. DOI: 10.1016/j.media.2014.10.004.
Paper accepted in Medical Image Analysis. Download it here.