医学图像分割的Mamba评估指标参考
- VM-UNet&VM-UNetV2
- LightM-UNet
- UltraLight-VM-UNet
- H-vmunet
- U-Mamba
- LMa-UNet
- Mamba-UNet
VM-UNet&VM-UNetV2
Dataset | Model | mIoU(%)↑ | DSC(%)↑ | Acc(%)↑ | Spe(%)↑ | Sen(%)↑ |
---|---|---|---|---|---|---|
ISIC17 | UNet | 76.98 | 86.99 | 95.65 | 97.43 | 86.82 |
ISIC17 | UTNetV2 | 77.35 | 87.23 | 95.84 | 98.05 | 84.85 |
ISIC17 | TransFuse | 79.21 | 88.40 | 96.17 | 97.98 | 87.14 |
ISIC17 | MALUNet | 78.78 | 88.13 | 96.18 | 98.47 | 84.78 |
ISIC17 | UNetV2 | 82.18 | 90.22 | 96.78 | 98.40 | 88.71 |
ISIC17 | VM-UNet | 80.23 | 89.03 | 96.29 | 97.58 | 89.90 |
ISIC17 | VM-UNetV2 | 82.34 | 90.31 | 96.70 | 97.67 | 91.89 |
Dataset | Model | mIoU(%)↑ | DSC(%)↑ | Acc(%)↑ | Spe(%)↑ | Sen(%)↑ |
---|---|---|---|---|---|---|
ISIC18 | UNet | 77.86 | 87.55 | 94.05 | 96.69 | 85.86 |
ISIC18 | UNet++ | 78.31 | 87.83 | 94.02 | 95.75 | 88.65 |
ISIC18 | Att-UNet | 78.43 | 87.91 | 94.13 | 96.23 | 87.60 |
ISIC18 | UTNetV2 | 78.97 | 88.25 | 94.32 | 96.48 | 87.60 |
ISIC18 | SANet | 79.52 | 88.59 | 94.39 | 95.97 | 89.46 |
ISIC18 | TransFuse | 80.63 | 89.27 | 94.66 | 95.74 | 91.28 |
ISIC18 | MALUNet | 80.25 | 89.04 | 94.62 | 96.19 | 89.74 |
ISIC18 | UNetV2 | 80.71 | 89.32 | 94.86 | 96.94 | 88.34 |
ISIC18 | VM-UNet | 81.35 | 89.71 | 94.91 | 96.13 | 91.12 |
ISIC18 | VM-UNetV2 | 81.37 | 89.73 | 95.06 | 97.13 | 88.60 |
Dataset | Model | DSC↑ | HD95↓ | Aorta | Gallbladder | Kidney(L) | Kidney® | Liver | Pancreas | Spleen | Stomach |
---|---|---|---|---|---|---|---|---|---|---|---|
Synapse | V-Net [25] | 68.81 | - | 75.34 | 51.87 | 77.10 | 80.75 | 87.84 | 40.05 | 80.56 | 56.98 |
Synapse | DARR [4] | 69.77 | - | 74.74 | 53.77 | 72.31 | 73.24 | 94.08 | 54.18 | 89.90 | 45.96 |
Synapse | R50 U-Net [10] | 74.68 | 36.87 | 87.47 | 66.36 | 80.60 | 78.19 | 93.74 | 56.90 | 85.87 | 74.16 |
Synapse | UNet [27] | 76.85 | 39.70 | 89.07 | 69.72 | 77.77 | 68.60 | 93.43 | 53.98 | 86.67 | 75.58 |
Synapse | R50 Att-UNet [10] | 75.57 | 36.97 | 55.92 | 63.91 | 79.20 | 72.71 | 93.56 | 49.37 | 87.19 | 74.95 |
Synapse | Att-UNet [26] | 77.77 | 36.02 | 89.55 | 68.88 | 77.98 | 71.11 | 93.57 | 58.04 | 87.30 | 75.75 |
Synapse | R50 ViT [10] | 71.29 | 32.87 | 73.73 | 55.13 | 75.80 | 72.20 | 91.51 | 45.99 | 81.99 | 73.95 |
Synapse | TransUnet [10] | 77.48 | 31.69 | 87.23 | 63.13 | 81.87 | 77.02 | 94.08 | 55.86 | 85.08 | 75.62 |
Synapse | TransNorm [5] | 78.40 | 30.25 | 86.23 | 65.10 | 82.18 | 78.63 | 94.22 | 55.34 | 89.50 | 76.01 |
Synapse | Swin U-Net [9] | 79.13 | 21.55 | 85.47 | 66.53 | 83.28 | 79.61 | 94.29 | 56.58 | 90.66 | 76.60 |
Synapse | TransDeepLab [6] | 80.16 | 21.25 | 86.04 | 69.16 | 84.08 | 79.88 | 93.53 | 61.19 | 89.00 | 78.40 |
Synapse | UCTransNet [32] | 78.23 | 26.75 | - | - | - | - | - | - | - | - |
Synapse | MT-UNet [33] | 78.59 | 26.59 | 87.92 | 64.99 | 81.47 | 77.29 | 93.06 | 59.46 | 87.75 | 76.81 |
Synapse | MEW-UNet [30] | 78.92 | 16.44 | 86.68 | 65.32 | 82.87 | 80.02 | 93.63 | 58.36 | 90.19 | 74.26 |
Synapse | VM-UNet | 81.08 | 19.21 | 86.40 | 69.41 | 86.16 | 82.76 | 94.17 | 58.80 | 89.51 | 81.40 |
Dataset | Model | mIoU(%)↑ | DSC(%)↑ | Acc(%)↑ | Spe(%)↑ | Sen(%)↑ |
---|---|---|---|---|---|---|
Kvasir-SEG | UNetV2 | 84.00 | 91.30 | 97.47 | 99.08 | 88.39 |
Kvasir-SEG | VMUnet | 80.32 | 89.09 | 96.80 | 98.49 | 87.21 |
Kvasir-SEG | VMUnetV2 | 84.15 | 91.34 | 97.52 | 99.25 | 87.71 |
ClinicDB | UNetV2 | 83.85 | 91.21 | 98.59 | 99.16 | 91.99 |
ClinicDB | VMUnet | 81.95 | 90.08 | 98.42 | 99.18 | 89.73 |
ClinicDB | VMUnetV2 | 89.31 | 94.35 | 99.09 | 99.38 | 95.64 |
ColonDB | UNetV2 | 57.29 | 72.85 | 96.19 | 98.43 | 68.46 |
ColonDB | VMUnet | 55.28 | 71.20 | 96.02 | 98.45 | 65.89 |
ColonDB | VMUnetV2 | 60.98 | 75.76 | 96.54 | 98.46 | 72.68 |
ETIS | UNetV2 | 71.90 | 83.65 | 98.35 | 98.61 | 92.96 |
ETIS | VMUnet | 66.41 | 79.81 | 98.26 | 99.33 | 75.79 |
ETIS | VMUnetV2 | 72.29 | 83.92 | 98.47 | 98.96 | 88.07 |
CVC-300 | UNetV2 | 82.86 | 90.63 | 99.34 | 99.54 | 93.82 |
CVC-300 | VMUnet | 79.55 | 88.61 | 99.20 | 99.44 | 92.46 |
CVC-300 | VMUnetV2 | 89.31 | 94.35 | 99.08 | 99.38 | 95.6 |