# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results
#### 4.3.1 Lung Segmentation
The top row illustrates the original CT scans from patients, depicting cross-sectional images of the thoracic cavity. These scans provide a clear view of the lung structures, bones, and surrounding tissues, with radiodensity variations allowing these components' differentiation. In the corresponding images of the bottom row, the results of the segmentation model are evident. Here, the lung regions have been delineated and filled in, starkly contrasting with the non-lung areas, which appear as black space. The segmentation model seems to have successfully extracted the lung regions from the CT images with high accuracy. In the first column, the model accurately identifies the lung boundaries and the separation between the left and right lungs. The second column shows a similarly precise segmentation, with the model correctly excluding the heart and the spine, which are centrally located in the thoracic cavity. In the third column, despite the more complex lung structures due to either a pathological condition or a scanning artifact, the model has managed to delineate the lung region, preserving the intricate details and the shape of the lungs. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **IoU** & **HD** \\ \hline Deeplabv3 plus [30] & 0.798 & 1.638 \\ \hline U-Net [33] & 0.762 & 1.914 \\ \hline U-Net++ [32] & 0.750 & 1.748 \\ \hline LDDNet [61] & 0.662 & 0.957 \\ \hline LGAN [62] & 0.632 & 0.940 \\ \hline
3D Res U-Net [63] & 0.623 & 2.835 \\ \hline Proposed w/o RL & 0.726 & 1.205 \\ \hline Proposed & 0.972 & 0.925 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of the proposed algorithm and various deep learning algorithms for lung segmentation on the LIDC-IDRI dataset. The reward structure of the lung segmentation model differentiates between accurate and inaccurate classifications, assigning values of \(\pm 1\) and \(\pm\lambda\) for the majority and minority classes, respectively. The optimal \(\lambda\) is tied to the class ratio and decreases as the majority-to-minority ratio increases. Performance was systematically evaluated over \(\lambda\) values ranging from 0 to 1 in 0.1 increments, with this assessment graphically represented in Figure 4a. As \(\lambda\) increases to about 0.4, both IoU and HD improve, indicating better accuracy for minority class predictions. However, beyond a \(\lambda\) of 0.4, performance declines, suggesting that too high a reward for the minority class may cause bias, leading to model overfitting or underfitting. The performance drop-off becomes significant as \(\lambda\) approaches 1, where the model heavily penalizes misclassifications of minority class samples, adversely affecting its generalization and accuracy. Figure 4b illustrates the progression of the agent's rewards over a series of episodes, highlighting its learning trajectory. Initially, the agent starts with a basic understanding, earning minimal rewards. As the episodes progress, there is a notable increase in rewards, especially between episodes 0 and 20, where the agent quickly learns essential rules and patterns. This phase shows a steep learning curve, transitioning from rudimentary knowledge to a more strategic approach. From episodes 20 to 60, rewards fluctuate but generally trend upwards, indicating the agent's ongoing adaptation and refinement of strategies. After episode 60, reward variability stabilizes, with values consistently around  
Figure 4: (a) Optimization of the \(\lambda\) parameter for lung segmentation model performance, (b) learning trajectory of the reward optimization of the agent on 100 episodes. Figure 3: Comparative visualization of original CT scans and segmented lung regions by the proposed model. 3 to 4, suggesting developing a reliable policy. This stability indicates well-established learning, although minor adjustments continue as the agent optimizes its strategies to meet evolving challenges and opportunities for further enhancement.