Name
Abstract | ||
|---|---|---|
In this study, a convex global optimization-based continuous max-flow (CGO-CMF) algorithm was proposed for the segmentation of bladder inner and outer walls on T2-weighted MR images (T2WI). Experimental results using 12 datasets of 3.0 T bladder T2WI datasets acquired from both volunteers and the patients with bladder cancer demonstrate a favorable performance and efficiency of the proposed approach for bladder segmentation, with the Dice similarity coefficient (DSC) of 89.8%, and an average time consumption of 1.2s without parallelized computation, which obviously outperformed the other traditional optimization-based approaches for bladder segmentation.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3364836.3364889 | Proceedings of the Third International Symposium on Image Computing and Digital Medicine |
Keywords | Field | DocType |
T2-weighted image, bladder segmentation, continuous max-flow, convex relaxation optimization | Global optimization,Pattern recognition,Computer science,Segmentation,Regular polygon,Bladder cancer,Image segmentation,Artificial intelligence,Dice,Computation | Conference |
ISBN | Citations | PageRank |
978-1-4503-7262-6 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaopan Xu | 1 | 0 | 0.34 |
| Peng Du | 2 | 0 | 0.34 |
| Yang Liu | 3 | 0 | 0.34 |
| Xi Zhang | 4 | 0 | 0.34 |
| Jing Yuan | 5 | 182 | 12.30 |
| Hongbing Lû | 6 | 5 | 4.49 |