Electron Microscopy Image Segmentation On
Metrics
Total Variation of Information
VI Merge
VI Split
Results
Performance results of various models on this benchmark
Model Name | 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 | - |
0 of 4 row(s) selected.