Vessel Segmentation on DRIVE
98.98AccuracyJebaseeli et al., 2019
Evaluation results
| Method | Links | ||||||
|---|---|---|---|---|---|---|---|
| Jebaseeli et al., 2019Supervision=Supervised2026.05 | 98.98 | — | — | — | 80.27 | 99.8 | |
| Sheet et al., 2013Supervision=Supervised2026.05 | 97.66 | — | — | — | — | — | |
| Rodrigues et al., 2020Supervision=Supervised2026.05 | 97.4 | — | — | — | 89.83 | 98.16 | |
| Jiang et al., 2019Supervision=Supervised2026.05 | 97.09 | — | — | — | 78.39 | 98.9 | |
| Jin et al., 2019Supervision=Supervised2026.05 | 96.97 | — | — | — | 78.94 | 98.7 | |
| Fan et al., 2016Supervision=Supervised2026.05 | 96.1 | — | — | — | 71.9 | 98.5 | |
| Lupascu et al., 2010Supervision=Supervised2026.05 | 95.97 | — | — | — | — | — | |
| Ricci et al., 2007Supervision=Supervised2026.05 | 95.95 | — | — | — | — | — | |
| Soomro et al., 2019Supervision=Supervised2026.05 | 95.6 | — | — | — | 87 | 98.5 | |
| Yan et al., 2018Supervision=Supervised2026.05 | 95.42 | — | — | — | 76.53 | 98.18 | |
| Li et al., 2016Supervision=Supervised2026.05 | 95.27 | — | — | — | 75.69 | 98.16 | |
| Adeyinka et al., 2019Supervision=Supervised2026.05 | 95.23 | — | — | — | 76.03 | — | |
| Mo et al., 2017Supervision=Supervised2026.05 | 95.21 | — | — | — | 77.79 | 97.8 | |
| Liskowski et al., 2016Supervision=Supervised2026.05 | 95.15 | — | — | — | 87.03 | 99.29 | |
| Fu et al., 2016Supervision=Supervised2026.05 | 94.7 | — | — | — | 72.94 | — | |
| Soares et al., 2006Supervision=Supervised2026.05 | 94.66 | — | — | — | — | — | |
| Marin et al., 2011Supervision=Supervised2026.05 | 94.52 | — | — | — | 70.67 | 98.01 | |
| Staal et al., 2004Supervision=Supervised2026.05 | 94.41 | — | — | — | — | — | |
| Al-Rawi et al., 2007Supervision=Supervised2026.05 | 94.2 | — | — | — | — | — | |
| Maji et al., 2016Supervision=Supervised2026.05 | 93.27 | — | — | — | — | — | |
| Shin et al., 2016Supervision=Supervised2026.05 | 92.71 | — | — | — | 92.55 | 93.82 | |
| Welikala et al., 2017Supervision=Supervised2026.05 | 91.99 | — | — | — | — | — | |
| Med-SAM-Adapter2026.01 | — | 71.2 | 55.3 | 23.4 | — | — | |
| MedSAM2026.01 | — | 19.4 | 10.8 | 87 | — | — | |
| MedSAM22026.01 | — | 66.6 | 50.1 | 43.8 | — | — | |
| Orlando et al., 2017Supervision=Supervised2026.05 | — | — | — | — | 78.97 | 96.84 | |
| Our (UniVG)number of training images=52026.04 | — | 80.32 | — | — | — | — | |
| RetFound2026.01 | — | 52.2 | 35.4 | 37.2 | — | — | |
| RetSAMEvaluation Protocol=Linear2026.01 | — | 79.3 | 65.8 | 34.5 | — | — | |
| RetSAMEvaluation Protocol=Finetune2026.01 | — | 81.3 | 68.6 | 36.8 | — | — | |
| SAM22026.01 | — | 75.5 | 60.7 | 29.1 | — | — | |
| SAM2-UNet2026.01 | — | 76.6 | 62.1 | 23.1 | — | — | |
| SAM32026.01 | — | 75.8 | 61.4 | 25.6 | — | — | |
| SOTA (Bhati et al., 2025)number of training images=202026.04 | — | 84.68 | — | — | — | — |