Table 6. Performance of the five best traditional and deep learning methods (see Tables 4,5) on the independent dataset (LIDC-IDRI).
| Method | DSC |
|---|---|
| U-Net-ResNet34 (full trained) | 0.763±0.217 |
| U-Net-MobileNet (full trained) | 0.692±0.320 |
| LinkNet-ResNet34 (full trained) | 0.738±0.229 |
| LinkNet-MobileNet (full trained) | 0.745±0.221 |
| PspNet-ResNet34 (full trained) | 0.657±0.318 |
| MorphACWE | 0.704±0.256 |
| Felzenszwalb | 0.627±0.250 |
| Watershed | 0.503±0.350 |
| MultiOtsu | 0.610±0.271 |
| MSER | 0.465±0.272 |
LIDC-IDRI, Lung Image Database Consortium image collection; DSC, Sørensen-Dice coefficient; MorphACWE, morphological active contours without edges; MSER, maximally stable extremal regions.