Our team works to advance the state of the art in efficient machine learning and computer vision, and marry them with medical expertise to help with computer-aided diagnosis, personalized treatment and efficient data management.
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.
- Ground Truth Segmentation Database (Siggraph '04, ECCV '04)
- Ben Glocker, Shahram Izadi, Jamie Shotton, and Antonio Criminisi, Real-Time RGB-D Camera Relocalization, in International Symposium on Mixed and Augmented Reality (ISMAR), IEEE, October 2013
- Darko Zikic, Ben Glocker, and Antonio Criminisi, Atlas Encoding by Randomized Forests for Efficient Label Propagation, in MICCAI 2013 - 16th Intl Conf. on Medical Image Computing and Computer Assisted Intervention (MICCAI Young Scientist Award), Springer, September 2013
- Darko Zikic, Ben Glocker, and Antonio Criminisi, Multi-Atlas Label Propagation with Atlas Encoding by Randomized Forests, in MICCAI 2013 Challenge Workshop on Segmentation: Algorithms, Theory and Applications (SATA) (Special "Left Field" Award), Springer, September 2013
- DongHye Ye, Darko Zikic, Ben Glocker, Antonio Criminisi, and Ender Konukoglu, Modality Propagation: Coherent Synthesis of Subject-Specific Scans with Data-Driven Regularization, in MICCAI 2013 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, September 2013
- Ben Glocker, Darko Zikic, Ender Konukoglu, David R. Haynor, and Antonio Criminisi, Vertebrae Localization in Pathological Spine CT via Dense Classification from Sparse Annotations, in MICCAI 2013 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, Springer, September 2013
- Jamie Shotton, Ben Glocker, Christopher Zach, Shahram Izadi, Antonio Criminisi, and Andrew Fitzgibbon, Scene Coordinate Regression Forests for Camera Relocalization in RGB-D Images, in Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, June 2013
- P. Kontschieder, P. Kohli, J. Shotton, and A. Criminisi, GeoF: Geodesic Forests for Learning Coupled Predictors, in Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, June 2013
- A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, Springer, February 2013
- E. Geremia, D. Zikic, O. Clatz, B.H. Menze, B. Glocker, E. Konukoglu, J. Shotton, O.M. Thomas, S.J. Price, T. Das, R. Jena, N. Ayache, and A. Criminisi, Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI, in Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013
- B. Menze, G. Langs, A. Montillo, Z. Tu, and A. Criminisi, Medical Computer Vision: recognition techniques and applications in medical imaging (2nd MICCAI-MCV workshop), Springer, 2013
If you have questions or comments, please contact us at firstname.lastname@example.org.