Research Article
Incorporating Adaptive Sparse Graph Convolutional Neural Networks for Segmentation of Organs at Risk in Radiotherapy
Table 2
Comparison between the network applied with the ASGCN and the related approaches.
| ROI | DSC of ASGCN | DSC of Swin UNETR | DSC of DCGCN | DSC of SE | DSC of AG | HD of ASGCN | HD of Swin UNETR | HD of DCGCN | HD of SE | HD of AG |
| Constrictor naris | 0.7722 | 0.7428 | 0.7738 | 0.7702 | 0.7774 | 1.7650 | 2.6254 | 1.7373 | 1.7634 | 1.6910 | Eyes | 0.8951 | 0.8855 | 0.8890 | 0.8968 | 0.8864 | 1.1163 | 1.1728 | 1.3087 | 1.1728 | 1.1584 | Lens | 0.6495 | 0.6554 | 0.6119 | 0.6454 | 0.6300 | 2.4956 | 1.4060 | 4.4368 | 4.3967 | 5.2654 | Optic nerves | 0.6418 | 0.6294 | 0.6326 | 0.6220 | 0.6372 | 2.3080 | 2.7100 | 2.6552 | 2.8887 | 2.5706 | Temporal lobes | 0.8690 | 0.7939 | 0.8577 | 0.8594 | 0.8579 | 2.1383 | 5.8505 | 3.3170 | 2.8090 | 3.6000 | Thyroids | 0.8089 | 0.7686 | 0.7990 | 0.7965 | 0.7886 | 2.0280 | 2.9508 | 4.8513 | 2.3406 | 2.3329 |
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Higher DSC and lower HD represent superior performance. The bold value denotes better performance.
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