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| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | UCTransNet | Northeastern University | 86.5 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) | | 🏆 | NexToU | HIT | 86.4 | [](https://arxiv.org/pdf/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 3 | MedNeXt | DKFZ | 83.1 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 4 | nnU-Net U-Net | DKFZ | 82.8 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 5 | UniSeg | NPU | 82.3 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 6 | MedFormer | Rutgers | 82.1 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 7 | STU-Net-B | Shanghai AI Lab | 82.1 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 8 | nnU-Net ResEncL | DKFZ | 81.4 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 9 | Diff-UNet | HKUST | 81.2 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 10 | SegVol | BAAI | 80.2 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 11 | LHU-Net | UR | 79.5 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 12 | Swin UNETR & CLIP | CityU | 78.1 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 13 | UNesT | NVIDIA | 77.7 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 14| Swin UNETR | NVIDIA | 77.2 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 15 | U-Net & CLIP | CityU | 77.1 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 16 | UNETR | NVIDIA | 76.5 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 17 | SAM-Adapter | Duke | 62.8 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | STU-Net-B | Shanghai AI Lab | 85.5 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 🏆 | MedFormer | Rutgers | 85.3 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 3 | MedNeXt | DKFZ | 85.3 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 4 | nnU-Net ResEncL | DKFZ | 84.9 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 5 | nnU-Net U-Net | DKFZ | 84.7 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 6 | UniSeg | NPU | 84.7 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 7 | LHU-Net | UR | 83.9 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 8 | Diff-UNet | HKUST | 83.8 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 9 | NexToU | HIT | 82.3 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 10 | U-Net & CLIP | CityU | 82.1 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 11 | Swin UNETR & CLIP | CityU | 80.2 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 12 | SegVol | BAAI | 79.3 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 13 | UCTransNet | Northeastern University | 77.8 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) | | 14 | Swin UNETR | NVIDIA | 76.9 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UNesT | NVIDIA | 75.1 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 16 | UNETR | NVIDIA | 74.7 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 17 | SAM-Adapter | Duke | 49.4 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | Diff-UNet | HKUST | 91.9 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 2 | MedFormer | Rutgers | 91.9 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 3 | nnU-Net ResEncL | DKFZ | 91.9 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 4 | STU-Net-B | Shanghai AI Lab | 91.9 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 5 | nnU-Net U-Net | DKFZ | 91.9 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 6 | LHU-Net | UR | 91.8 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 7 | MedNeXt | DKFZ | 91.8 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 8 | SegVol | BAAI | 91.8 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 9 | UniSeg | NPU | 91.5 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 10 | U-Net & CLIP | CityU | 91.1 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 11 | Swin UNETR & CLIP | CityU | 91.0 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 12 | UNesT | NVIDIA | 90.1 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 13 | Swin UNETR | NVIDIA | 89.7 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 14 | NexToU | HIT | 89.6 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 15 | UNETR | NVIDIA | 89.2 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 16 | SAM-Adapter | Duke | 87.3 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) | | 17 | UCTransNet | Northeastern University | 86.9 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | Diff-UNet | HKUST | 92.8 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 2 | MedFormer | Rutgers | 92.8 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 3 | nnU-Net U-Net | DKFZ | 92.7 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 4 | MedNeXt | DKFZ | 92.6 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 5 | nnU-Net ResEncL | DKFZ | 92.6 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 6 | LHU-Net | UR | 92.5 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 7 | STU-Net-B | Shanghai AI Lab | 92.5 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 8 | SegVol | BAAI | 92.5 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 9 | UniSeg | NPU | 92.2 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 10 | U-Net & CLIP | CityU | 91.9 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 11 | Swin UNETR & CLIP | CityU | 91.7 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 12 | UNesT | NVIDIA | 90.9 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 13 | SAM-Adapter | Duke | 90.4 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) | | 14 | NexToU | HIT | 90.1 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 15 | UNETR | NVIDIA | 90.1 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 16 | Swin UNETR | NVIDIA | 89.8 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 17 | UCTransNet | Northeastern University | 86.5 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | MedFormer | Rutgers | 96.4 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 2 | MedNeXt | DKFZ | 96.3 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 3 | nnU-Net ResEncL | DKFZ | 96.3 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 4 | LHU-Net | UR | 96.2 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 5 | nnU-Net U-Net | DKFZ | 96.2 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 6 | Diff-UNet | HKUST | 96.2 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 7 | STU-Net-B | Shanghai AI Lab | 96.2 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 8 | UniSeg | NPU | 96.1 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 9 | U-Net & CLIP | CityU | 96.0 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 10 | SegVol | BAAI | 96.0 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 11 | Swin UNETR & CLIP | CityU | 95.8 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 12 | NexToU | HIT | 95.7 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 13 | UNesT | NVIDIA | 95.3 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 14 | Swin UNETR | NVIDIA | 95.2 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UNETR | NVIDIA | 95.0 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 16 | SAM-Adapter | Duke | 94.1 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) | | 17 | UCTransNet | Northeastern University | 93.6 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | MedNeXt | DKFZ | 83.3 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 2 | STU-Net-B | Shanghai AI Lab | 83.2 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 3 | MedFormer | Rutgers | 83.1 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 4 | nnU-Net ResEncL | DKFZ | 82.9 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 5 | UniSeg | NPU | 82.7 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 6 | nnU-Net U-Net | DKFZ | 82.3 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 7 | Diff-UNet | HKUST | 81.9 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 8 | LHU-Net | UR | 81.0 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 9 | U-Net & CLIP | CityU | 80.8 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 10 | Swin UNETR & CLIP | CityU | 80.2 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 11 | NexToU | HIT | 80.2 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 12 | SegVol | BAAI | 79.1 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 13 | UNesT | NVIDIA | 76.2 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 14 | Swin UNETR | NVIDIA | 75.6 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UNETR | NVIDIA | 72.3 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 16 | UCTransNet | Northeastern University | 59.0 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) | | 17 | SAM-Adapter | Duke | 50.2 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | STU-Net-B | Shanghai AI Lab | 81.3 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 🏆 | MedNeXt | DKFZ | 81.3 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 3 | UniSeg | NPU | 81.2 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 4 | nnU-Net U-Net | DKFZ | 81.0 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 5 | Diff-UNet | HKUST | 80.8 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 6 | MedFormer | Rutgers | 80.7 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 7 | nnU-Net ResEncL | DKFZ | 80.5 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 8 | LHU-Net | UR | 79.4 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 9 | U-Net & CLIP | CityU | 78.5 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 10 | NexToU | HIT | 78.1 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 11 | SegVol | BAAI | 77.8 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 12 | Swin UNETR & CLIP | CityU | 76.8 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 13 | Swin UNETR | NVIDIA | 75.4 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 14 | UNesT | NVIDIA | 74.4 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UNETR | NVIDIA | 71.5 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UCTransNet | Northeastern University | 68.1 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) | | 17 | SAM-Adapter | Duke | 48.0 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | MedFormer | Rutgers | 95.5 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 2 | nnU-Net ResEncL | DKFZ | 95.2 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 3 | MedNeXt | DKFZ | 95.2 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 4 | nnU-Net U-Net | DKFZ | 95.1 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 5 | STU-Net-B | Shanghai AI Lab | 95.1 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 6 | Diff-UNet | HKUST | 95.0 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 7 | LHU-Net | UR | 94.9 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 8 | UniSeg | NPU | 94.9 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 9 | NexToU | HIT | 94.7 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 10 | SegVol | BAAI | 94.5 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 11 | U-Net & CLIP | CityU | 94.3 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 12 | Swin UNETR & CLIP | CityU | 94.1 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 13 | UNesT | NVIDIA | 93.2 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 14 | Swin UNETR | NVIDIA | 92.7 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UNETR | NVIDIA | 91.7 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 16 | SAM-Adapter | Duke | 90.5 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) | | 17 | UCTransNet | Northeastern University | 90.2 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) |

| rank | model | organization | DSC | paper | github | |:---|:---|:---|:---:|:---:|:---:| | 🏆 | STU-Net-B | Shanghai AI Lab | 93.5 | [](https://arxiv.org/pdf/2304.06716) | [](https://github.com/uni-medical/STU-Net) | | 2 | MedNeXt | DKFZ | 93.5 | [](https://arxiv.org/pdf/2303.09975) | [](https://github.com/MIC-DKFZ/MedNeXt) | | 3 | nnU-Net ResEncL | DKFZ | 93.4 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 4 | MedFormer | Rutgers | 93.4 | [](https://arxiv.org/abs/2203.00131) | [](https://github.com/yhygao/CBIM-Medical-Image-Segmentation) | | 5 | UniSeg | NPU | 93.3 | [](https://arxiv.org/abs/2304.03493) | [](https://github.com/yeerwen/UniSeg) | | 6 | nnU-Net U-Net | DKFZ | 93.3 | [](https://arxiv.org/abs/1809.10486) | [](https://github.com/MIC-DKFZ/nnUNet) | | 7 | Diff-UNet | HKUST | 93.1 | [](https://arxiv.org/abs/2303.10326) | [](https://github.com/ge-xing/Diff-UNet) | | 8 | LHU-Net | UR | 93.0 | [](https://arxiv.org/abs/2404.05102) | [](https://github.com/xmindflow/LHUNet) | | 9 | NexToU | HIT | 92.7 | [](https://arxiv.org/abs/2305.15911) | [](https://github.com/PengchengShi1220/NexToU) | | 10 | SegVol | BAAI | 92.5 | [](https://arxiv.org/abs/2311.13385) | [](https://github.com/BAAI-DCAI/SegVol) | | 11 | U-Net & CLIP | CityU | 92.4 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 12 | Swin UNETR & CLIP | CityU | 92.2 | [](https://arxiv.org/abs/2301.00785) | [](https://github.com/ljwztc/CLIP-Driven-Universal-Model) | | 13 | UNesT | NVIDIA | 90.9 | [](https://arxiv.org/abs/2303.10745) | [](https://github.com/Project-MONAI/tutorials) | | 14 | Swin UNETR | NVIDIA | 90.5 | [](https://arxiv.org/abs/2211.11537) | [](https://github.com/Project-MONAI/tutorials) | | 15 | UNETR | NVIDIA | 88.8 | [](https://arxiv.org/abs/2111.04004) | [](https://github.com/Project-MONAI/tutorials) | | 16 | SAM-Adapter | Duke | 88.0 | [](https://arxiv.org/abs/2404.09957) | [](https://github.com/mazurowski-lab/finetune-SAM) | | 17 | UCTransNet | Northeastern University | 81.9 | [](https://arxiv.org/abs/2109.04335) | [](https://github.com/McGregorWwww/UCTransNet) |

git clone https://github.com/MrGiovanni/Touchstone
cd Touchstone

conda env create -f environment.yml
source activate touchstone
python -m ipykernel install --user --name touchstone --display-name "touchstone"

cd notebooks
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.kernel_name=touchstone TotalSegmentatorMetadata.ipynb
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.kernel_name=touchstone DAPAtlasMetadata.ipynb
#results: plots are saved inside Touchstone/outputs/plotsTotalSegmentator/ and Touchstone/outputs/plotsDAPAtlas/

cd ../plot
python AggregatedBoxplot.py --stats
#results: Touchstone/outputs/summary_groups.pdf

``` totalsegmentator_results ├── Diff-UNet │ ├── dsc.csv │ └── nsd.csv ├── LHU-Net │ ├── dsc.csv │ └── nsd.csv ├── MedNeXt │ ├── dsc.csv │ └── nsd.csv ├── ... dapatlas_results ├── Diff-UNet │ ├── dsc.csv │ └── nsd.csv ├── LHU-Net │ ├── dsc.csv │ └── nsd.csv ├── MedNeXt │ ├── dsc.csv │ └── nsd.csv ├── ... ```

#statistical significance maps (Appendix D.2.3):
python PlotAllSignificanceMaps.py
python PlotAllSignificanceMaps.py --organs second_half
python PlotAllSignificanceMaps.py --nsd
python PlotAllSignificanceMaps.py --organs second_half --nsd
#results: Touchstone/outputs/heatmaps

cd ../notebooks
jupyter nbconvert --to notebook --execute --ExecutePreprocessor.kernel_name=touchstone GroupAnalysis.ipynb
#results: Touchstone/outputs/box_plots

The csv results files in totalsegmentator_results/ and dapatlas_results/ contain per-sample dsc and nsd scores. Rich meatdata for each one of those samples (sex, age, scanner, diagnosis,...) are available in metaTotalSeg.csv and 'Clinical Metadata FDG PET_CT Lesions.csv', for TotalSegmentator and DAP Atlas, respectively. The code in TotalSegmentatorMetadata.ipynb and DAPAtlasMetadata.ipynb extracts this meatdata into simplfied group lists (e.g., a list of all samples representing male patients), and saves these lists in the folders plotsTotalSegmentator/ and plotsDAPAtlas/. You can modify the code to generate custom sample lists (e.g., all men aged 30-35). To compare a set of groups, the filenames of all lists in the set should begin with the same name. For example, comp1_list_a.pt, comp1_list_b.pt, comp1_list_C.pt can represent a set of 3 groups. Then, PlotGroup.py can draw boxplots and perform statistical tests comparing the AI algorithm's results (dsc and nsd) for the samples inside the different custom lists you created. In our example, you just just need to specify --group_name comp1 when running PlotGroup.py: ```bash python plot/PlotGroup.py --ckpt_root totalsegmentator_results/ --group_root outputs/plotsTotalSegmentator/ --group_name comp1 --organ liver --stats ```

@inproceedings{bassi2024touchstone,
  title={Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?},
  author={Bassi, Pedro RAS and Li, Wenxuan and Tang, Yucheng and Isensee, Fabian and Wang, Zifu and Chen, Jieneng and Chou, Yu-Cheng and Kirchhoff, Yannick and Rokuss, Maximilian and Huang, Ziyan and Ye, Jin and He, Junjun and Wald, Tassilo and Ulrich, Constantin and Baumgartner, Michael and Roy, Saikat and Maier-Hein, Klaus H. and Jaeger, Paul and Ye, Yiwen and Xie, Yutong and Zhang, Jianpeng and Chen, Ziyang and Xia, Yong and Xing, Zhaohu and Zhu, Lei and Sadegheih