Retinal Vessel Segmentation on DRIVE (test)
97.9AccuracyRV-GAN
Evaluation results
| Method | Links | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RV-GANYear=20202021.05 | 97.9 | — | — | — | 98.87 | — | — | — | — | — | — | — | |
| U-Net with heavy data augmentationYear=20212021.05 | 97.12 | — | — | — | 98.55 | — | — | — | — | — | — | — | |
| MERIT-GCASCADE2023.10 | 97.07 | — | 82.81 | 98.44 | — | — | 82.9 | 70.81 | — | — | — | — | |
| Our SGLYear=2021, K=82021.03 | 97.05 | — | 83.8 | 98.34 | 98.86 | — | 83.16 | — | — | — | — | — | |
| FR-UNet2023.10 | 97.05 | — | 83.56 | 98.37 | — | — | 83.16 | 71.2 | — | — | — | — | |
| BEFD-UNetYear=20202021.03 | 97.01 | — | 82.15 | 98.45 | 98.67 | — | 82.67 | — | — | — | — | — | |
| SA-UNetYear=20202021.03 | 96.98 | — | 82.12 | 98.4 | 98.64 | — | 82.63 | — | — | — | — | — | |
| DUNetType=DNN, Year=20182018.11 | 96.97 | 85.37 | 78.94 | 98.7 | 98.56 | — | — | — | — | — | — | — | |
| Our BaselineYear=20212021.03 | 96.95 | — | 83.41 | 98.27 | 98.67 | — | 82.62 | — | — | — | — | — | |
| MERIT-CASCADE2023.10 | 96.89 | — | 82.94 | 98.22 | — | — | 82.21 | 69.08 | — | — | — | — | |
| PVT-GCASCADE2023.10 | 96.89 | — | 83 | 98.22 | — | — | 82.1 | 69.7 | — | — | — | — | |
| U-NetYear=20182021.05 | 96.81 | — | — | — | 98.3 | — | — | — | — | — | — | — | |
| UNet++2023.10 | 96.79 | — | 78.91 | 98.5 | — | — | 81.14 | 68.27 | — | — | — | — | |
| PVT-CASCADE2023.10 | 96.79 | — | 83.07 | 98.1 | — | — | 81.73 | 69.1 | — | — | — | — | |
| UNet2023.10 | 96.78 | — | 80.57 | 98.33 | — | — | 81.41 | 68.64 | — | — | — | — | |
| Attention UNet2023.10 | 96.62 | — | 79.06 | 98.31 | — | — | 80.39 | 67.21 | — | — | — | — | |
| PVTV2-b2baseline=encoder only2023.10 | 96.24 | — | 82.02 | 97.61 | — | — | 79.14 | 65.48 | — | — | — | — | |
| DCNNYear=20202022.07 | 95.93 | — | 71.19 | 98.32 | — | — | — | — | — | — | — | — | |
| IterNetYear=20202021.03 | 95.74 | — | 77.91 | 98.31 | 98.13 | — | 82.18 | — | — | — | — | — | |
| IterNetYear=20192021.05 | 95.74 | — | — | — | 98.16 | — | — | — | — | — | — | — | |
| Wang et al.Year=20192021.05 | 95.73 | — | — | — | 98.14 | — | — | — | — | — | — | — | |
| Dual E-UNetYear=20192021.03 | 95.67 | — | 79.4 | 98.16 | 97.72 | — | 82.7 | — | — | — | — | — | |
| LadderNetYear=20182021.03 | 95.61 | — | 78.56 | 98.1 | 97.93 | — | 82.02 | — | — | — | — | — | |
| LadderNetYear=20182022.07 | 95.61 | — | 78.56 | 98.1 | — | — | — | — | — | — | — | — | |
| BCDU-Netd=32019.08 | 95.6 | — | 80.07 | 97.86 | 97.89 | 82.24 | — | — | — | — | — | — | |
| SUD-GANYear=20202021.05 | 95.6 | — | — | — | 97.86 | — | — | — | — | — | — | — | |
| BCDU-NetYear=20192022.07 | 95.6 | — | 80.07 | 97.86 | — | — | — | — | — | — | — | — | |
| BCDU-Netd=12019.08 | 95.59 | — | 80.12 | 97.84 | 97.88 | 82.22 | — | — | — | — | — | — | |
| Alom et al.Type=DNN, Year=20182018.11 | 95.56 | — | 77.92 | 98.13 | 97.84 | — | — | — | — | — | — | — | |
| R2U-Net2019.08 | 95.56 | — | 77.92 | 98.13 | 97.82 | 81.71 | — | — | — | — | — | — | |
| R2U-NetYear=20182021.03 | 95.56 | — | 77.92 | 98.13 | 97.84 | — | 81.71 | — | — | — | — | — | |
| R2U-netYear=20182022.07 | 95.56 | — | 77.51 | 98.16 | — | — | — | — | — | — | — | — | |
| RU-net2019.08 | 95.53 | — | 77.26 | 98.2 | 97.79 | 81.49 | — | — | — | — | — | — | |
| Sun et al.Year=20202021.05 | 95.45 | — | — | — | 97.88 | — | — | — | — | — | — | — | |
| Sun et al.Year=20202022.07 | 95.45 | — | 82.09 | 97.41 | — | — | — | — | — | — | — | — | |
| GeethaRamani et al.Year=20162022.07 | 95.36 | — | 70.79 | 97.78 | — | — | — | — | — | — | — | — | |
| Liskowski et al.Type=DNN, Year=20162018.11 | 95.35 | — | 78.11 | 98.07 | 97.9 | — | — | — | — | — | — | — | |
| Dasgupta et al.Type=DNN, Year=20172018.11 | 95.33 | 84.98 | 76.91 | 98.01 | 97.44 | — | — | — | — | — | — | — | |
| U-net2019.08 | 95.31 | — | 75.37 | 98.2 | 97.55 | 81.42 | — | — | — | — | — | — | |
| U-netYear=20182022.07 | 95.31 | — | 75.37 | 98.2 | — | — | — | — | — | — | — | — | |
| CcNetYear=20202021.05 | 95.28 | — | — | — | 96.78 | — | — | — | — | — | — | — | |
| Li et al.Type=DNN, Year=20152018.11 | 95.27 | — | 75.69 | 98.16 | 97.38 | — | — | — | — | — | — | — | |
| Cross-Modality2019.08 | 95.27 | — | 75.69 | 98.16 | 97.38 | — | — | — | — | — | — | — | |
| Fu et al.Type=DNN, Year=20162018.11 | 95.23 | — | 76.03 | — | — | — | — | — | — | — | — | — | |
| Roychowdhury et al.Type=STA, Year=20172018.11 | 95.2 | — | 72.5 | 98.3 | 96.2 | — | — | — | — | — | — | — | |
| Roychowdhury et al.Year=20152022.07 | 95.2 | — | 72.5 | 98.3 | — | — | — | — | — | — | — | — | |
| Deep Model2019.08 | 94.95 | — | 77.63 | 97.68 | 97.2 | — | — | — | — | — | — | — | |
| Christodoulidis et al.Year=20162022.07 | 94.79 | — | 85.06 | 95.82 | — | — | — | — | — | — | — | — | |
| Chen et al.Type=DNN, Year=20172018.11 | 94.53 | — | 74.26 | 97.35 | 95.16 | — | — | — | — | — | — | — | |
| Azzopardi et al.Type=STA, Year=20152018.11 | 94.42 | — | 76.55 | 97.04 | 96.14 | — | — | — | — | — | — | — | |
| COSFIRE filters2019.08 | 94.42 | — | 76.55 | 97.048 | 96.14 | — | — | — | — | — | — | — | |
| Azzopardi et al.Year=20152022.07 | 94.42 | — | 76.55 | 97.05 | — | — | — | — | — | — | — | — | |
| DeepLabV3Training Regime=Few-Shot Transfer, Specific Modules=ResNet, ASPP, Examples Num=32024.03 | — | — | — | — | — | — | 32.1 | — | — | — | — | — | |
| DeterministicTime (s)=0.082026.04 | — | — | — | — | 94.4 | — | 73.6 | — | 24.5 | 50 | — | — | |
| Ensemble (N=5)Ensemble size (N)=5, Time (s)=3.902026.04 | — | — | — | — | 94.7 | — | 77.1 | 62.7 | 3.1 | 83.3 | — | — | |
| Faster RCNNTraining Regime=Few-Shot Transfer, Specific Modules=ResNet, RPN, Examples Num=42024.03 | — | — | — | — | — | — | 25.4 | — | — | — | — | — | |
| GiT-B multi-taskTraining Regime=Few-Shot Transfer, Specific Modules=None, Examples Num=12024.03 | — | — | — | — | — | — | 34.3 | — | — | — | — | — | |
| GiT-B universalTraining Regime=Few-Shot Transfer, Specific Modules=None, Examples Num=12024.03 | — | — | — | — | — | — | 51.1 | — | — | — | — | — | |
| GiT-H universalTraining Regime=Few-Shot Transfer, Specific Modules=None, Examples Num=12024.03 | — | — | — | — | — | — | 57.9 | — | — | — | — | — | |
| GiT-L universalTraining Regime=Few-Shot Transfer, Specific Modules=None, Examples Num=12024.03 | — | — | — | — | — | — | 55.4 | — | — | — | — | — | |
| MC Drop. (adaptive)Decision Strategy=adaptive, Stochastic passes (T)=30, Time (s)=2.652026.04 | — | — | — | — | 96.5 | — | 76.3 | 61.7 | 3.5 | 72.2 | 42.4 | 25 | |
| MC Drop. (conf-aware)Decision Strategy=conf-aware, Stochastic passes (T)=30, Time (s)=2.652026.04 | — | — | — | — | 96.5 | — | 76.3 | 61.7 | 3.5 | 72.2 | 48.8 | 10.9 | |
| MC Drop. (global)Decision Strategy=global, Stochastic passes (T)=30, Time (s)=2.652026.04 | — | — | — | — | 96.5 | — | 76.3 | 61.7 | 3.5 | 72.2 | 26.5 | 33.4 | |
| Residual UNetYear=20192021.05 | — | — | — | — | 97.79 | — | — | — | — | — | — | — | |
| TTA (adaptive)Decision Strategy=adaptive, Deterministic augmentations (K)=6, Time (s)=0.862026.04 | — | — | — | — | 96.9 | — | 76.8 | 62.4 | 3.5 | 88.1 | 79.5 | 25 | |
| TTA (conf-aware)Decision Strategy=conf-aware, Deterministic augmentations (K)=6, Time (s)=0.862026.04 | — | — | — | — | 96.9 | — | 76.8 | 62.4 | 3.5 | 88.1 | 55.1 | 12.1 | |
| TTA (global)Decision Strategy=global, Deterministic augmentations (K)=6, Time (s)=0.862026.04 | — | — | — | — | 96.9 | — | 76.8 | 62.4 | 3.5 | 88.1 | 55.1 | 13.2 | |
| U-NetTraining Regime=Supervised, Specific Modules=None, Examples Num=12024.03 | — | — | — | — | — | — | 81.4 | — | — | — | — | — |