Name
Abstract | ||
|---|---|---|
In this study, we propose an efficient non-rigid MR-TRUS deformable registration method to improve the accuracy of targeting suspicious locations during a 3D ultrasound (US) guided prostate biopsy. The proposed deformable registration approach employs the multi-channel modality independent neighbourhood descriptor (MIND) as the local similarity feature across the two modalities of MR and TRUS, and a novel and efficient duality-based convex optimization based algorithmic scheme is introduced to extract the deformations which align the two MIND descriptors. The registration accuracy was evaluated using 10 patient images by measuring the TRE of manually identified corresponding intrinsic fiducials in the whole gland and peripheral zone, and performance metrics (DSC, MAD and MAXD) for the apex, mid-gland and base of the prostate were also calculated by comparing two manually segmented prostate surfaces in the registered 3D MR and TRUS images. Experimental results show that the proposed method yielded an overall mean TRE of 1.74 mm, which is favorably comparable to a clinical requirement for an error of less than 2.5 mm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-40811-3_25 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Non-rigid Image Registration,Convex Optimization,MR-TRUS prostate registration,MIND Similarity Measurement | Computer vision,Fiducial marker,Pattern recognition,Computer science,Prostate biopsy,Duality (optimization),Artificial intelligence,Convex optimization,3D ultrasound,Peripheral zone | Conference |
Volume | Issue | ISSN |
8149 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
16 | 0.70 | 13 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Sun | 1 | 16 | 0.70 |
| Jing Yuan | 2 | 372 | 23.02 |
| Martin Rajchl | 3 | 421 | 34.67 |
| Wu Qiu | 4 | 203 | 18.54 |
| Cesare Romagnoli | 5 | 67 | 9.65 |
| Aaron Fenster | 6 | 106 | 8.46 |