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.