Coronary Artery Segmentation on DCA1
76.21DSCSE-RegUNet 4GF
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| SE-RegUNet 4GFReference=(Chang et al., 2024)2025.10 | 76.21 | — | — | — | — | |
| Multilayer Perception (MLP) ArchitectureYear=2019, Fold Strategy=Training Set: 100 images, Test Set: 30 images2025.10 | 68.57 | 96.98 | — | 63.64 | 98.8 | |
| RobustSAMPrompt type=point prompts, Prompt count (K)=3, Inference mode=single-image, Input condition=degraded2026.04 | 31.3 | — | — | — | — | |
| RobustMedSAMPrompt type=point prompts, Prompt count (K)=3, Inference mode=single-image, Input condition=degraded2026.04 | 29.7 | — | — | — | — | |
| SAMPrompt type=point prompts, Prompt count (K)=3, Inference mode=single-image, Input condition=degraded2026.04 | 27.2 | — | — | — | — | |
| RobustMedSAM+SVDPrompt type=point prompts, Prompt count (K)=3, Inference mode=single-image, Input condition=degraded2026.04 | 21.7 | — | — | — | — | |
| MedSAMPrompt type=point prompts, Prompt count (K)=3, Inference mode=single-image, Input condition=degraded2026.04 | 10.7 | — | — | — | — | |
| U-net with a novel Loss FunctionYear=2021, Fold Strategy=-2025.10 | — | 97 | — | — | — |