Name
Abstract | ||
|---|---|---|
An efficient and accurate segmentation of 3D end-firing transrectal ultrasound (TRUS) images plays a central role in the planning and treatment of 3D TRUS guided prostate biopsy. In this paper, we propose a novel convex optimization based approach to delineate prostate boundaries from 3D TRUS images. The technique makes use of the approximate rotational symmetry of prostate shapes and reduces the original 3D segmentation problem to a sequence of simple 2D segmentation sub-problems by means of rotationally reslicing the 3D TRUS images. In practice, this significantly decreases the computational load, facilitates introducing learned shape information and improves segmentation efficiency and accuracy. For each 2D resliced frame, we introduce a new convex optimization based contour evolution method to locate the 2D slicewise prostate boundary subject to the additional shape constraint. The proposed contour evolution method provides a fully time implicit scheme to move the contour to its globally optimal position at each discrete time, which allows a large evolving time step-size to accelerate convergence. Moreover, the proposed algorithm is implemented on a GPU to achieve a high performance. Quantitative validations on twenty 3D TRUS patient prostate images demonstrate that the proposed approach can obtain a DSC of 93.7 +/- 2.5%, a sensitivity of 91.2 +/- 3.1%, a MAD of 1.37 +/- 0.3mm, and a MAXD of 3.02 +/- 0.44mm. The mean segmentation time for the dataset was 18.3 +/- 2.5s, in addition to 25s for initialization. Our proposed method exhibits the advantages of accuracy, efficiency and robustness compared to the level set and active contour based methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1117/12.2006836 | Proceedings of SPIE |
Keywords | Field | DocType |
prostate segmentation,convex optimization,shape constraint,rotational volume reslicing,3D ultrasound | Active contour model,Computer vision,Scale-space segmentation,Segmentation,Computer science,Level set,Robustness (computer science),Artificial intelligence,Initialization,Discrete time and continuous time,Convex optimization | Conference |
Volume | ISSN | Citations |
8669 | 0277-786X | 2 |
PageRank | References | Authors |
0.40 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wu Qiu | 1 | 203 | 18.54 |
| Jing Yuan | 2 | 372 | 23.02 |
| Eranga Ukwatta | 3 | 154 | 18.10 |
| David Tessier | 4 | 28 | 2.85 |
| Aaron Fenster | 5 | 270 | 67.27 |