Data and Tools for Comparing Algorithms
An increased dependence on medical imaging for patient diagnosis and treatment places new challenges upon the clinical community. Focusing on the curative treatment of patients with high precision radiotherapy, the volume of image data associated with treatment has increased by two orders of magnitude in the last five years. Current image processing workflows struggle to keep up with the pace at which imaging technology is developing.
Microsoft Research is working 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.
Software tools
GrabCut for the assisted segmentation of 2-D images
An efficient, interactive tool for foreground segmentation in still images
- The ground truth Segmentation Database (Siggraph '04, ECCV '04) is available from the project page.
GeoS for the assisted segmentation of 3-D medical scans
A very easy-to-use, free tool for the segmentation of anatomical regions within 2-D and 3-D medical images, such as CT, X-ray, and MR scans
- Download GeoS version 2.2
- Read the GeoS user guide (PDF file)
- Read the paper: Geodesic Image Segmentation
- Do you have feedback on GeoS? Please let us know!
- Darko Zikic, Ben Glocker, and Antonio Criminisi, Atlas Encoding by Randomized Forests for Efficient Label Propagation, in MICCAI - 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 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, 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 - 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
- N. Pittman, A. Forin, A. Criminisi, J. Shotton, and A. Mahram, Image Segmentation Using Hardware Forest Classifiers, in Intl Symp. on Field-Programmable Custom Computing Machines (FCCM), IEEE, 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
- Ender Konukoglu, Ben Glocker, Darko Zikic, and Antonio Criminisi, Neighbourhood Approximation using Randomized Forests, in Medical Image Analysis, Elsevier, 2013
Related links
Contact us
If you have questions or comments, please contact us at miic@microsoft.com.