Name
Title | ||
|---|---|---|
Patient-specific semi-supervised learning for postoperative brain tumor segmentation. |
Abstract | ||
|---|---|---|
In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semisupervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre-and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10404-1_89 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Computer vision,Semi-supervised learning,Pattern recognition,Computer science,Segmentation,Glioma,Brain tumor segmentation,Image segmentation,Artificial intelligence,Random forest,Magnetic resonance imaging | Conference | 8673 |
Issue | ISSN | Citations |
Pt 1 | 0302-9743 | 4 |
PageRank | References | Authors |
0.41 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raphael Meier | 1 | 307 | 14.51 |
| Stefan Bauer | 2 | 371 | 14.87 |
| Johannes Slotboom | 3 | 12 | 2.40 |
| Roland Wiest | 4 | 344 | 22.73 |
| Mauricio Reyes | 5 | 73 | 13.74 |