# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results
#### 4.3.1 Lung Segmentation  
Our GAN-based model performs exceptionally precisely segmenting images, especially in accurately delineating lung regions against the surrounding anatomy. To evaluate its effectiveness, we conducted a detailed comparison with six cutting-edge models: Deeplabv3 plus [30], U-Net [33], U-Net++ [32], LDDNet [61], LGAN [62], and 3D Res U-Net [63], with the comparative results encapsulated in Table 2. Within the range of models compared, Deeplabv3 plus takes the lead with an IoU score of 0.798, indicating the highest degree of overlap between its predicted segmentation and the actual ground truth. Close contenders, U-Net and U-Net++, exhibit strong segmentation capabilities with IoU scores of 0.762 and 0.750, respectively. In contrast, LDDNet and LGAN show less \begin{table}
\begin{tabular}{l c c} \hline \hline
**Hyperparameter** & **Range** & **Optimal Value** \\ \hline \hline \multicolumn{3}{c}{Lung segmentation} \\ \hline Learning rate (G) & 0.0001–0.1 & 0.01 \\ Number of layers (G) & 5–20 & 12 \\ Learning rate (D) & 0.0001–0.1 & 0.01 \\ Number of layers (D) & 5–20 & 12 \\ \(\lambda\) & 0–1 & 0.4 \\ Batch size & 16–128 & 64 \\ Momentum & 0.8–0.99 & 0.9 \\ Weight decay & 0.00001–0.001 & 0.0001 \\ \hline \hline \multicolumn{3}{c}{Tumor detection} \\ \hline Learning rate (Mask R-CNN) & 0.00001–0.01 & 0.001 \\ RPN anchor scales & 32–512 & 128 \\ RPN anchor ratios & 0.5–2 & 1 \\ ROI positive ratio & 0.2–0.5 & 0.33 \\ Bounding box refinement std & 0.01–0.1 & 0.05 \\ Mask threshold & 0.5–0.7 & 0.6 \\ \hline \hline \multicolumn{3}{c}{3D reconstruction} \\ \hline Learning rate (GAN) & 0.00001–0.01 & 0.0002 \\ Number of LSTM units & 100–1000 & 512 \\ LSTM layers & 1–5 & 3 \\ Dropout rate (LSTM) & 0.0–0.5 & 0.25 \\ Beta1 (Adam optimizer) & 0.5–0.9 & 0.5 \\ Beta2 (Adam optimizer) & 0.999 & 0.999 \\ Batch size & 8–64 & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hyperparameters for lung segmentation, tumor detection, and 3D reconstruction models. precision in their segmentations, reflected in lower IoU scores of 0.662 and 0.632. However, when considering the Hausdorff Distance, LGAN and LDDNet outperform others with the lowest scores, suggesting that their segmentations are closer to the ground truth at their furthest point. Interestingly, 3D Res U-Net, despite its lower IoU, presents the highest HD score of 2.835, hinting at potential irregularities in its segmentation boundaries. Our proposed GAN-based model, however, marks a significant advancement with an IoU of 0.972, indicating a 21.8% improvement over the best-performing comparison model, and an HD score of 0.925, the lowest of the group, which translates to a 67.4% improvement from the highest HD score of the compared models. The comparison becomes even more striking when our model is evaluated against its variant without RL. The incorporation of RL has led to a notable IoU increase from 0.726 to 0.972, a leap of 33.9%, signifying a superior match with the ground truth. Similarly, the enhancement in HD from 1.205 to 0.925, a reduction of 23.2%, underscores the heightened accuracy of boundary detection with RL integration. To ascertain the statistical significance of our proposed model's superiority over existing models, we conducted paired \(t\)-tests on IoU and HD metrics. The tests yielded \(p\)-values such as 0.002 for IoU when comparing our model to Deeplabv3 plus and 0.005 for HD when comparing to LGAN, indicating significant improvements. These results confirm the enhancements are significant and statistically robust, supporting the model's superior performance. Additionally, the 95% confidence intervals, like those ranging from 0.15 to 0.21 for IoU differences with Deeplabv3 plus, affirm these findings. This statistical evidence strongly validates the proposed model's utility in clinical settings for more accurate and reliable lung segmentation. Figure 3 displays three sets of images produced by the proposed lung segmentation model.