Through the creation of new applications and platforms, our team works to accelerate the rate of research in areas such as machine learning and computer vision making it easier for scientists to access large test datasets and compare algorithms against common benchmarks. In collaboration with clinicians, we are working towards the goal of making medical images understandable to a computer.
We work with top research institutes around the world to make data and tools available and advance the state of the art in automatic analysis of medical scans.
GeoS for the assisted segmentation of 3-D medical scans
A very easy-to-use, free Windows application for the segmentation of anatomical regions within 2-D and 3-D medical images, such as CT, X-ray, and MR scans
CodaLab is an open source platform that enables researchers to rigorously compare the accuracy of image analysis algorithms with respect to one another.
- A. Ghosh, V. Wottschel, E. Kaden, J. Zhang, H. Zhang, S. N. Sotiropoulos, D. Zikic, T. B. Dyrby, A. Criminisi, and D. C. Alexander, Generalisability of Image Quality Transfer: Can we approximate in-vivo human brains from dead monkey brains?, in Intl Society for Magnetic Resonance in Medicine (ISMRM), May 2016.
- Y. Ioannou, D. Robertson, D. Zikic, P. Kontschieder, J. Shotton, M. Brown, and A. Criminisi, Decision Forests, Convolutional Networks and the Models In-Between, in arXiv:1603.01250, March 2016.
- C. Morrison, K. Huckvale, B. Corish, J. Dorn, P. Kontschieder, K. O'Hara, ASSESS MS Team, A. Criminisi, and A. Sellen, Assessing Multiple Sclerosis with Kinect: Designing Computer Vision Systems for Real-World Use, in Human-Computer Interaction, January 2016.
- S. Blessenohl, C. Morrison, A. Criminisi, and J. Shotton, Improving Indoor Mobility of the Visually Impaired with Depth-Based Spatial Sound, in ICCV-ACVR workshop, December 2015.
- M. D'Souza, J. Burggraaff, P. Kontschieder, J. Dorn, C.P.Kamm, S. Seinheimer, P. Tewarie, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, B Uitdehaag, and L. Kappos, Prediction of expanded disability status scale subscores of motor dysfunction in multiple sclerosis using depth-sensing computer vision, in Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), October 2015.
- H. Lombaert, A. Criminisi, and N. Ayache, Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation, in Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer, October 2015.
- J. Burggraaff, J. Dorn, M. D'Souza, C. P. Kamm, P. Tewarie, P. Kontschieder, C. Morrison, A. Sellen, A. Criminisi, F. Dahlke, L. Kappos, and B. M. J. Uitdehaag, Video-based paired-comparison ranking: a validation tool for fine-grained measurements of motor dysfunction in multiple sclerosis, in Congress of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), October 2015.
- J. Margeta, A. Criminisi, R. Cabrera Lozoya, D. C. Lee, and N. Ayache, Finetuned convolutional neural nets for cardiac MRI acquisition plane recognition, in journal Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, June 2015.
- Kenton O'Hara, Gerardo Gonzalez, Abigail Sellen, Graeme Penney, Varnavas, Helena Mentis, Antonio Criminisi, Robert Corish, Mark Rouncefield, Neville Dastur, and Tom Carrell, Touchless Interaction in Surgery, in Communications of the ACM, December 2014.
- D. Alexander, D. Zikic, J. Zhang, H. Zhang, and A. Criminisi, Image Quality Transfer via Random Forest Regression: Applications in Diffusion MRI, in MICCAI 2014 - Intl Conf. on Medical Image Computing and Computer Assisted Intervention, Springer, October 2014.
If you have questions or comments, please contact us at firstname.lastname@example.org.