# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 2 Related Work
### Lung Segmentation  
[PERSON] et al. [30] delve into deep learning applications for diagnosing diseases such as COVID-19 and tuberculosis by analyzing pulmonary X-rays, explicitly employing the deeplabv3 plus CNN-based semantic segmentation model that utilizes \"Atrous Convolution\" to preserve feature resolution in deeper layers of the network. [PERSON] et al. [31] tackle the pressing issue of diagnosing lung cancer at advanced stages by employing deep learning to improve early detection rates, particularly in high-risk individuals. Their methodology involves initial preprocessing of CT images with the Wiener filter, followed by segmentation using a GAN trained with the Salp Shuffled Shepherd Optimization Algorithm (SSSOA), and subsequent classification via the VGG16 CNN model. [PERSON] et al. [32] present an innovative approach to lung segmentation in X-ray images using U-Net++, aiming to boost tuberculosis detection rates. This novel method surpasses traditional techniques and mitigates data leakage problems, significantly enhancing diagnostic precision. [PERSON] et al. [33] unveil a modified U-Net architecture tailored for semantic lung segmentation from chest X-rays, integrating multiple dropouts in deconvolutional layers to avoid overfitting, thereby ensuring high accuracy and model generalization with a streamlined framework. [PERSON] et al. [34] validate the U-Net framework's capability in segmenting lung areas from X-ray images, attaining a notable average Intersection over Union (IoU) score of 92.82. [PERSON] et al. [35] introduce a sophisticated automated segmentation method that merges radionics with manually selected and algorithmically derived features, achieving a Dice similarity index of 89.42% on the ILD database MedGIFT. [PERSON] et al. [36] develop an advanced automatic segmentation system that incorporates a 3D V-Net and a Spatial Transform Network (STN) to accurately segment lung tissue in CT scans, facilitating the diagnosis of COVID-19 by examining textures and other critical features. [PERSON] et al. [37] utilize a U-Net framework optimized through Bayesian methods on datasets from Japan and Montgomery, securing high Dice Similarity Coefficients (DSC) of 0.976 and 0.973, respectively. [PERSON] et al. [38] propose a unique U-Net variant designed for the automated detection of COVID-19 infections in CT scans, validated on actual patient data from Pedro Ernesto University Hospital in Rio de Janeiro. [PERSON] [39] enhances the conventional U-Net structure by incorporating a Variational Auto-Encoder (VAE) in each encoder-decoder layer, markedly improving the network's feature extraction efficiency. This advanced network is evaluated using NIH and JRST datasets, demonstrating exceptional accuracy and F1 scores of 0.9701 and 0.9334 for the former and 0.9750 and 0.9578 for the latter, respectively.  
Many current lung segmentation techniques involve classifying each pixel as either lung tissue or not, which presents a challenge due to class imbalance; the overwhelming majority of pixels represent non-lung tissue. Acknowledging this issue, our paper introduces an RL-based innovative approach to lung segmentation that circumvents the problem of unbalanced classification.