Name
Abstract | ||
|---|---|---|
In this paper, we propose a new semantic segmentation network called ’E-Res U-Net’, to achieve better segmentation results of deep and superficial muscles in ultrasonic muscle images. This model is based on U-Net, and its structure has been modified to improve the performance of the algorithm. There are three aspects of improvement based on U-Net, including E-Res layer, dilated convolution module, and E-Res path. Additional experiments demonstrate that each designed module in our proposed network is effective, can improve the accuracy compared to the original U-Net. When compared with other algorithms which are state-of-the-art, the experimental result under the overall network structure is even more excellent. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2021.115625 | Expert Systems with Applications |
Keywords | DocType | Volume |
Dilated convolution module,Residual learning,Ultrasound image,Muscle image segmentation | Journal | 185 |
ISSN | Citations | PageRank |
0957-4174 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junsheng Zhou | 1 | 0 | 0.34 |
| Yiwen Lu | 2 | 2 | 1.37 |
| Siyi Tao | 3 | 0 | 0.34 |
| Xuan Cheng | 4 | 0 | 0.68 |
| Chenxi Huang | 5 | 10 | 3.17 |