Electron Microscopy Image Segmentation On
평가 지표
Total Variation of Information
VI Merge
VI Split
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | Total Variation of Information | VI Merge | VI Split | Paper Title | Repository |
|---|---|---|---|---|---|
| Waterz (3D U-Net) | 0.807 | 0.236 | 0.571 | Biologically-Constrained Graphs for Global Connectomics Reconstruction | - |
| U-Net | - | - | - | U-Net: Convolutional Networks for Biomedical Image Segmentation | - |
| DTN | - | - | - | Dense Transformer Networks for Brain Electron Microscopy Image Segmentation | - |
| Waterz (3D U-Net) + Refinement | 0.647 | 0.209 | 0.438 | Biologically-Constrained Graphs for Global Connectomics Reconstruction | - |
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