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. Margeta, A.Criminisi, D.C.Lee, and N.Ayache, Recognizing Cardiac Magnetic Resonance Acquisition Planes using Finetuned Convolutional Neural Networks, in To appear in Computer Methods in Biomechanics and Biomedical Engineering, December 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 Souza M, Burggraaf J, Kamm C, Tewarie P, Kontschieder P, Dorn J, Morrison C, Vogel T, Sellen A, Machacek M, Chin P, Criminisi A, Dahlke F, Uitdehaag B, and Kappos L, Infrared depth sensor based automated classification of motor dysfunction in multiple sclerosis - a proof-of-concept study, 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
- 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
- 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
- D. Zikic, B. Glocker, and A. Criminisi, Encoding Atlases by Randomized Classification Forests for Efficient Multi-Atlas Label Propagation, in Medical Image Analysis, Elsevier, June 2014
- M. D Souza, C. Kamm, J. Burggraaff, P. Tewarie, B. Glocker, J. Dorn, T. Vogel, C. Morrison, A. Sellen, M. Machacek, P. Chin, B. Uitdehaag, antcrim, F. Dahlke, C. Polman, and L. Kappos, Assessment of Disability in Multiple Sclerosis Using the Kinect-Camera System: A Proof-of-Concept Study, in American Academy of Neurology Annual Meeting (AAN), April 2014
- J. Margeta, A. Criminisi, D. C. Lee, and N. Ayache, Recognizing Cardiac Magnetic Resonance Acquisition Planes, in MIUA 2014 - Medical Image Understanding and Analysis, 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, 2014
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