Table 1.
Performance comparison between our models and state-of-the-art autocontouring tools.
| Segmentation Method | Dataset | Right Parotid | Left Parotid | Submandibular |
|---|---|---|---|---|
| Dice±SD | Dice±SD | Dice±SD | ||
| CNN (Rhee et al.39) | ~1000 (real) | 82.3±6.5 | 82.4±4.8 | NA |
| V-Net | ~1000 (real) | 81.4±5.9 | 81.3±5.5 | NA |
| MACS (Yang et al37)/MACS-AS (Yang et al42) | 12 (real)/20 (real) | 84.5±9.2 | 84.1±7.9 | 74.0±7.6 |
| PCA 10 | 2000 (synthetic) | 82.8±6.8 | 82.0±6.9 | 74.2±6.8 |
| PCA 20 | 2000 (synthetic) | 82.9±7.1 | 82.5±7.2 | 75.5±6.3 |
| PCA 30 | 2000 (synthetic) | 83.1±6.1 | 82.9±6.7 | 76.3±7.7 |
The best Dice results of PCA 10, 20, and 30 were chosen for comparison. MACS, multi-atlas contouring service; MACS-AS multi-atlas contouring service with atlas selection; V-Net means our V-Net model trained with the same training dataset of Rhee et al39. PCA, principal component analysis. PCA 10 means that 10 PCA models were used to create synthetic CT scans; SD: standard deviation.