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.
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
- 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
- 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
- A. Criminisi and J. Shotton, Decision Forests for Computer Vision and Medical Image Analysis, Springer, February 2013
- Ender Konukoglu, Ben Glocker, Darko Zikic, and Antonio Criminisi, Neighbourhood Approximation using Randomized Forests, in Medical Image Analysis, Elsevier, 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
- Jamie Shotton, Ross Girshick, Andrew Fitzgibbon, Toby Sharp, Mat Cook, Mark Finocchio, Richard Moore, Pushmeet Kohli, Antonio Criminisi, Alex Kipman, and Andrew Blake, Efficient Human Pose Estimation from Single Depth Images, in Decision Forests for Computer Vision and Medical Image Analysis, Springer, 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
- A. Criminisi, D. Robertson, O. Pauly, B. Glocker, E. Konukoglu, J. Shotton, D. Mateus, A. Martinez Möller, S.G. Nekolla, and N. Navab, Anatomy Detection and Localization in 3D Medical Images, in Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013
- Ender Konukoglu, Ben Glocker, Antonio Criminisi, and Kilian M. Pohl, WESD - Weighted Spectral Distance for Measuring Shape Dissimilarity, in Transactions Pattern Analysis and Machine Intelligence (PAMI), IEEE, 2013
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