Name
Abstract | ||
|---|---|---|
A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories: normal, tumor, and normal but progressed to tumor at a later time. In this preliminary study, we focused on the trend of brain tumor progression during three consecutive visits, i.e., visit A, B, and C. A machine learning algorithm was trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit A to visit C. Preliminary results showed that prediction for brain tumor progression is feasible. An average of 80.9% pixel-wise accuracy was achieved for tumor progression prediction at visit C. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.844035 | Proceedings of SPIE |
Keywords | Field | DocType |
Tumor Progression Prediction,Machine Learning,DTI | Tumor progression,Computer vision,Feature vector,Diffusion MRI,Computer science,Brain tumor,Artificial intelligence,Pixel,Machine learning,Magnetic resonance imaging | Conference |
Volume | ISSN | Citations |
7624 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuzhong Shen | 1 | 184 | 21.96 |
| Debrup Banerjee | 2 | 2 | 1.16 |
| jiang li | 3 | 23 | 9.88 |
| Adam Chandler | 4 | 6 | 2.26 |
| Frederic D Mckenzie | 5 | 75 | 18.51 |
| J Wang | 6 | 27 | 14.15 |