Table 5. The Numerical Performance of Three Post-Processed Models for BMs Detection and Segmentation.
| After post-processing | Standard U-Net | Modified U-Net | Modified U-Net with GAN | |
|---|---|---|---|---|
| LWS (%) | 87.84 | 89.19 | 89.19 | |
| Average FPR | 2.2 | 1.4 | 0.9 | |
| Patient-wise DSC | 0.834 (±0.08)* (p=0.017) | 0.868 (±0.05)* (p=0.019) | 0.873 (±0.05)* (p=0.037) | |
| Tumor volume range (lesion-wise DSC) | ||||
| ≥0.1 cc | 0.754 (±0.27)* (p<0.001) | 0.746 (±0.28)* (p<0.001) | 0.752 (±0.28)* (p<0.001) | |
| 0.06–<0.1 cc | 0.775 (±0.05) | 0.788 (±0.08) | 0.787 (±0.08) | |
| 0.04–<0.06 cc | 0.576 (±0.34) | 0.667 (±0.27) | 0.718 (±0.17) | |
| 0.02–<0.04 cc | 0.638 (±0.22)* (p=0.002) | 0.607 (±0.20)* (p=0.002) | 0.630 (±0.17)* (p=0.003) | |
| <0.02 cc | 0.534 (±0.34) | 0.575 (±0.38) | 0.586 (±0.37) | |
BMs, brain metastases; GAN, generative adversarial network; LWS, lesion-wise sensitivity; FPR, false-positive rate; DSC, dice similarity coefficient.
Asterisks (*) indicate the statistical significance of differences between the post-processed model and the corresponding model before post-processing, as determined by p-values.