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.
- 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.
- 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.
- 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.
- C. Morrison, K. Huckvale, A. Sakar, P. Kontschieder, J. Dorn, S. Steinheimer, C. P. Kamm, J. Burggraaff, M. D'Souza, F. Dahlke, L. Kappos, B. Uitdehaag, A. Criminisi, and A. Sellen, Visualizing ubiquitously sensed measures of motor ability in multiple sclerosis for clinical use, 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.
- Darko Zikic, Ben Glocker, and Antonio Criminisi, Classifier-based Multi-Atlas Label Propagation with Test-specific Atlas Weighting for Correspondence-free Scenarios, in MICCAI Workshop on Medical Computer Vision: Algorithms for Big Data (bigMCV), Springer, September 2014.
- P. Kontschieder, J.F. Dorn, C. Morrison, R. Corish, D. Zikic, A. Sellen, M. DSouza, C. P. Kamm, J. Burggraaff, P. Tewarie, T. Vogel, M. Azzarito, P. Chin, F. Dahlke, C. Polman, L. Kappos, B. Uitdehaag, and A. Criminisi, Quantifying Progression of Multiple Sclerosis via Classification of Depth Videos, in MICCAI 2014 - Intl Conf. on Medical Image Computing and Computer Assisted Intervention, Springer, September 2014.
- Burggraaf J, D Souza M, Dorn J, Kamm C, Tewarie P, Kontschieder P, Morrison C, Vogel T, Sellen A, Machacek M, Chin P, Criminisi A, Dahlke F, Kappos L, and Uitdehaag B, Video-based paired-comparison ranking: A validation tool for fine grained measures of motor dysfunction in multiple sclerosis, in European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), September 2014.
- Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth Gerstner, Marc-Andre Weber, Tal Arbel, Brian B. Avants, Nicholas Ayache, Patricia Buendia, D. Louis Collins, Nicolas Cordier, Jason J. Corso, Antonio Criminisi, Tilak Das, Herve Delingette, Cagatay Demiralp, Christopher R. Durst, Michel Dojat, Senan Doyle, Joana Festa, Florence Forbes, Ezequiel Geremia, Ben Glocker, Polina Golland, Xiaotao Guo, Andac Hamamci, Khan M. Iftekharuddin, Raj Jena, Nigel M. John, Ender Konukoglu, Danial Lashkari, Jose Antonio Mariz, Raphael Meier, Sergio Pereira, Doina Precup, Stephen J. Price, Tammy Riklin Raviv, Syed M. S. Reza, Michael Ryan, Duygu Sarikaya, Lawrence Schwartz, Hoo-Chang Shin, Jamie Shotton, Carlos A. Silva, Nuno Sousa, Nagesh K. Subbanna, Gabor Szekely, Thomas J. Taylor, Owen M. Thomas, Nicholas J. Tustison, Gozde Unal, Flor Vasseur, Max Wintermark, Dong Hye Ye, Liang Zhao, Binsheng Zhao, Darko Zikic, Marcel Prastawa, Mauricio Reyes, and Koen Van Leemput, The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), in IEEE Transactions on Medical Imaging, September 2014.
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