# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results
#### 4.3.3 Three-Dimensional Reconstruction
These low \(p\)-values confirm that the differences in performance metrics between the proposed and existing models are not due to random chance, indicating a genuine improvement in 3D tumor reconstruction. The robust statistical analysis supports the claim of superiority of the proposed model over traditional approaches. Table 5 highlights the computational efficiency of various 3D reconstruction models, a critical factor for applications requiring rapid processing. It provides a comparative analysis of runtime and GPU usage, offering key insights for those in the medical imaging and 3D reconstruction fields. The table shows YOLOv4 as moderately efficient with a runtime of 2235 s, indicating a balance between complexity and speed. In contrast, GAN-ResNet-3D has a much higher runtime of 3895 s, suggesting it may not be ideal for time-sensitive applications due to its complex processing. On the GPU usage front, GAN-LSTM-3D uses 10.2 GB, indicating efficiency, while GAN-GK-LSTM, the highest at 15.6 GB, suggests greater complexity. Our proposed model has a balanced runtime of 2475 s and uses 10.7 GB of GPU. It shows a good trade-off between performance and resource usage, making it adaptable for various clinical settings, even those with limited computational resources. Figure 6 presents a side-by-side comparison of the original 3D configurations of ten tumor samples against their digitally reconstructed counterparts. This juxtaposition underscores the complexities involved in replicating the seamless contours that characterize the true forms of the tumors. Traditional methods often falter at this juncture, unable to faithfully reproduce the smooth perimeters, which is crucial for precise reconstructions. However, our proposed method effectively navigates these common pitfalls. Employing advanced techniques and refined algorithms, it adeptly retains the smoothness of the tumor boundaries, thereby ensuring reconstructions that are both accurate and true to the original shapes. This achievement marks a substantial leap forward in 3D tumor imaging reconstruction. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **HD** & **ED** \\ \hline YOLOv4 [20] & 1.146 & 1.236 \\ \hline GAN-LSTM-3D [22] & 1.163 & 1.270 \\ \hline GAN-GK-LSTM [55] & 1.182 & 1.494 \\ \hline GAN-ResNet-3D [52] & 2.260 & 1.852 \\ \hline ANN-GGO [53] & 2.630 & 2.023 \\ \hline Proposed w/o VGG-16 & 1.342 & 1.438 \\ \hline Proposed w/o SP & 0.975 & 1.186 \\ \hline Proposed with GANDA & 0.983 & 1.132 \\ \hline Proposed with AEDA & 1.035 & 1.205 \\ \hline Proposed & 0.986 & 1.126 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of the proposed model versus comparator models for lung tumor 3D reconstruction on the LIDC-IDRI dataset. Our investigation into the performance of diverse pre-trained models on 3D lung tumor reconstruction has revealed significant variations in accuracy. The experiment encompassed models such as AlexNet, GoogleNet, ResNet, DenseNet, and MobileNet, with VGG-16 as the reference model. According to Table 6, the precision of VGG-16 emerges as the highest, evidenced by its lowest scores in both HD and ED. The contrast is striking when we compare the improvement in precision that VGG-16 offers to that of other models. The HD and ED of AlexNet are elevated by approximately 72.5% and 29%, respectively, over those of VGG-16, underscoring the enhanced capability of the latter to detail tumor contours precisely. As we progress through models from GoogleNet to MobileNet, there is  
Figure 6: Comparison of original and reconstructed 3D tumor shapes highlighting the effectiveness of the proposed reconstruction method in preserving boundary smoothness.