Vertebra Identification on VerSe 2019 (test)
98.12Identification RateMulti-View Vertebra Localization and Identification
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Multi-View Vertebra Localization and IdentificationInput=2D multi-view projection, Backbone=ResNet-502023.07 | 98.12 | — | — | |
| Chen M.Input=3D2023.07 | 96.94 | — | — | |
| Multi-View Vertebra Localization and IdentificationInput=2D multi-view projection, Backbone=ResNet-502023.07 | 96.45 | — | — | |
| Payer C.Input=3D2023.07 | 95.65 | — | — | |
| Payer C.Input=3D2023.07 | 94.25 | — | — | |
| Payer C.Type=VerSe challenge entry2023.07 | 94.25 | 4.8 | — | |
| Single-head GNNConfiguration=(9x1)2023.07 | 93.02 | 1.43 | — | |
| Lessmann N.Input=3D2023.07 | 90.42 | — | — | |
| Lessmann N.Type=VerSe challenge entry2023.07 | 90.42 | 7.04 | — | |
| Sekuboyina A.Input=2D MIP2023.07 | 89.97 | — | — | |
| Lessmann N.Input=3D2023.07 | 89.86 | — | — | |
| Sekuboyina A.Input=2D MIP2023.07 | 87.66 | — | — | |
| With legitimacy predictiongamma=102023.07 | 87.51 | 1.32 | 81.69 | |
| Chen M.Input=3D2023.07 | 86.73 | — | — | |
| Chen M.Type=VerSe challenge entry2023.07 | 86.73 | 7.13 | — | |
| Hidden MarkovType=Baseline2023.07 | 49.06 | 1.45 | — |