Name
Title | ||
|---|---|---|
Global context and boundary structure-guided network for cross-modal organ segmentation |
Abstract | ||
|---|---|---|
•We firstly propose to utilize global context to guide deformable convolution, which can obtain reasonable receptive fields with a global perspective.•We introduce the class-wise global context to handle intensity non-uniformities in cross-modal organ segmentation.•A novel loss which focuses on the areas near the boundary is proposed here and can deal with the border blurs well. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ipm.2020.102252 | Information Processing & Management |
Keywords | DocType | Volume |
Cross-modal,Organ segmentation,Global context,Boundary structure,Loss function | Journal | 57 |
Issue | ISSN | Citations |
4 | 0306-4573 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaonan Guo | 1 | 8 | 5.27 |
| Hongtao Xie | 2 | 439 | 47.79 |
| Hai Xu | 3 | 0 | 1.35 |
| Yongdong Zhang | 4 | 263 | 27.77 |