Abstract
This paper presents an approach for joint segmentation and deformable registration of brain scans of glioma patients to a normal atlas. The proposed method is based on the Expectation Maximization (EM) algorithm that incorporates a glioma growth model for atlas seeding, a process which modifies the normal atlas into one with a tumor and edema. The modified atlas is registered into the patient space and utilized for the posterior probability estimation of various tissue labels. EM iteratively refines the estimates of the registration parameters, the posterior probabilities of tissue labels and the tumor growth model parameters. We have applied this approach to 10 glioma scans acquired with four Magnetic Resonance (MR) modalities (T1, T1-CE, T2 and FLAIR ) and validated the result by comparing them to manual segmentations by clinical experts. The resulting segmentations look promising and quantitatively match well with the expert provided ground truth.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 532-540 |
| Number of pages | 9 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 6892 LNCS |
| Issue number | PART 2 |
| DOIs | |
| State | Published - 2011 |
| Externally published | Yes |
| Event | 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada Duration: Sep 18 2011 → Sep 22 2011 |
Keywords
- EM
- diffusion-reaction model
- joint segmentation-registration
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science