LungCTSeg-Net: Lung HRCT Image Segmentation Using Learning Approach

Background: Automatic lung segmentation is a crucial initial step in computer-aided lung computed tomography (CT) diagnosis. However, existing methods struggle to achieve accurate segmentation in the presence of dense abnormalities, limiting their clinical reliability. Objectives: This research aims to improve lung segmentation accuracy by proposing a novel generative adversarial network (GAN)-founded method, named LungCTSeg-Net, designed to effectively segment lungs even in the presence of dense abnormalities. Materials and Methods: The projected LungCTSeg-Net processes input lung CT pieces through a series of encoders, converting them into feature plots. A specialized multi-scale dense feature extraction (MSDFE) module abstracts multi-scale features from these encoded maps. The segmentation map is generated using decoders, with repeated down-sampling and upsampling to ensure invariance to the size of dense anomalies. The method is tested on the publicly available interstitial lung disease (ILD) dataset. Results: Experimental results demonstrate that LungCTSeg-Net achieves robust performance regardless of the presence of dark anomalies in lung CT scans, outperforming existing approaches in segmentation accuracy. Conclusion: The proposed LungCTSeg-Net approach improves lung segmentation accuracy in challenging cases with dense anomalies. The combination of multi-scale feature extraction and GAN architecture enhances the model’s capability to capture composite lung constructions, supporting more reliable computer-aided lung CT diagnosis.