# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results
#### 4.3.4 Analysis of Generalization  
To rigorously assess the generalization capabilities of our proposed model, we conducted experiments on the LUng Nodule Analysis (LUNA16) dataset [55] and the Medical Imaging and Data Resource Center (MIDRC) [69], comparing lung segmentation, tumor detection, and 3D reconstruction performance against other models. The results for these categories are detailed in Tables 7-9 for both the LUNA16 and MIDRC datasets. For lung segmentation, our model significantly outperformed competitors such as Deeplabv3 plus, U-Net, and U-Net++ on both datasets, achieving an IoU 22% higher than Deeplabv3 plus on the LUNA16 dataset and 20% higher on the MIDRC dataset. Its HD was reduced by 41% on LUNA16 and 39% on MIDRC, demonstrating superior precision in segmenting lung tissues, which is critical for early detection and accurate medical assessments. In tumor detection, our model excelled on both datasets, outperforming advanced models like DEHA-Net and DS-CMSF with higher IoU and significantly lower HD. It improved IoU by approximately 4.5% over DEHA-Net on LUNA16 and by 4.3% on MIDRC, and it reduced HD by 14% on LUNA16 and 12% on MIDRC, showcasing its precise ability to delineate tumor boundaries. In 3D tumor reconstruction, our model was significantly superior, outperforming GAN-based approaches like GAN-LSTM-3D and GAN-GK-LSTM on both datasets. It improved HD by 21% compared to GAN-LSTM-3D on LUNA16, indicating highly accurate reproduction of 3D tumor structures, and by 20% on MIDRC. These findings consistently underscore our model's ability to maintain a high performance across different datasets, affirming its strong generalization capability and suitability for deployment in diverse clinical environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LUNA16**} & \multicolumn{2}{c}{**MIDRC**} \\ \cline{2-5}  & **IoU** & **HD** & **IoU** & **HD** \\ \hline Deeplabv3 plus [30] & 0.780 & 1.750 & 0.742 & 1.953 \\ \hline U-Net [33] & 0.740 & 1.960 & 0.702 & 2.124 \\ \hline U-Net++ [32] & 0.730 & 1.850 & 0.692 & 2.063 \\ \hline LDDNet [61] & 0.650 & 1.000 & 0.611 & 1.232 \\ \hline LGAN [62] & 0.620 & 0.970 & 0.582 & 1.126 \\ \hline
3D Res U-Net [63] & 0.610 & 2.950 & 0.574 & 3.124 \\ \hline Proposed & 0.955 & 1.035 & 0.926 & 1.146 \\ \hline \end{tabular}
\end{table}
Table 7: Performance metrics of various deep learning algorithms for lung segmentation on the LUNA16 and MIDRC datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**LUNA16**} & \multicolumn{2}{c}{**MIDRC**} \\ \cline{2-5}  & **IoU** & **HD** & **IoU** & **HD** \\ \hline D DEHA-Net [64] & 0.850 & 0.760 & 0.823 & 0.782 \\ \hline DS-CMSF [65] & 0.830 & 0.790 & 0.801 & 0.810 \\ \hline DA-Net [66] & 0.780 & 0.910 & 0.753 & 0.942 \\ \hline R-CNN [67] & 0.760 & 1.250 & 0.736 & 1.2713 \\ \hline PN-SAMPM [68] & 0.740 & 1.450 & 0.713 & 1.4796 \\ \hline Proposed & 0.880 & 0.650 & 0.868 & 0.672 \\ \hline \end{tabular}
\end{table}
Table 8: Performance metrics of various deep learning algorithms for tumor detection on the LUNA16 and MIDRC datasets.