The Microsoft researchers are working with the University of Cambridge to develop tools to assist surgeons and oncologists for treating patients with glioblastoma. These tools provides a highly effective means of computer-assisted segmentation and fully automatic, 3-D tumor delineation.
Currently, physicians will take a MRI scan of a brain and carefully draw an outline on each 2-D slice of the tumor and its constituent parts: areas of actively growing tumor, areas of tumor that have grown so fast that they have run out of nutrients and oxygen and are starting to die, and the area of brain surrounding the tumor that shows signs of swelling and inflammation. The researchers have devised an algorithm to replicate the manual annotations and to generalize on a previously unseen patient-data set.
The system can be trained to compute the segmentation accurately and efficiently. The technique used to segment the tumors into their component parts is a discriminative approach based on the use of decision forests using context-aware spatial features. Individual tissue types are classified simultaneously, and the results are computationally efficient, with low model complexity.
Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas OM, Das T, Jena R, & Price SJ (2012). Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention, 15 (Pt 3), 369-76 PMID: 23286152