Name
Title | ||
|---|---|---|
Recursive Centerline- and Direction-Aware Joint Learning Network with Ensemble Strategy for Vessel Segmentation in X-ray Angiography Images |
Abstract | ||
|---|---|---|
•A new multi-task learning network joining vessel direction and CDTM auxiliary tasks is presented for vessel segmentation.•A recursive learning framework is introduced to progressively boost vessel segmentation performance without extra network parameters.•A complementary-task ensemble strategy is designed by fusing the outputs of the three tasks with training only one model.•The method is evaluated on the XRA images of the coronary artery and aorta.•Compared with the six other state-of-the-art methods, the method achieves the most complete and accurate vessel segmentation results. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cmpb.2022.106787 | Computer Methods and Programs in Biomedicine |
Keywords | DocType | Volume |
vessel segmentation,X-ray angiography,multi-task learning,recursive learning,ensemble | Journal | 220 |
ISSN | Citations | PageRank |
0169-2607 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Han | 1 | 0 | 0.34 |
| Danni Ai | 2 | 7 | 5.52 |
| Yining Wang | 3 | 0 | 0.34 |
| Yonglin Bian | 4 | 0 | 0.34 |
| Ruirui An | 5 | 0 | 0.34 |
| Jingfan Fan | 6 | 53 | 14.09 |
| Hong Song | 7 | 8 | 8.34 |
| Hongzhi Xie | 8 | 1 | 1.74 |
| Jian Yang | 9 | 283 | 48.62 |