Edge-Enhanced Feature Pyramid SwinUNet: Advanced Segmentation of Lung Nodules in CT Images

Abstract

In the field of oncology, lung cancer is a leading contributor to cancer-related mortality, highlighting the need for early detection of lung nodules for effective intervention. However, accurate segmentation of lung nodules in Computed Tomography (CT) images remains a significant challenge due to issues such as heterogeneous nodule dimensions, low contrast, and their visual similarity with surrounding tissues. To address these challenges, this study proposes the Edge-Enhanced Feature Pyramid SwinUNet (EE-FPS-UNet), an advanced segmentation model that integrates a modified Swin Transformer with a feature pyramid network (FPN). The research objective is to enhance boundary delineation and multi-scale feature aggregation for improved segmentation performance. The proposed model uses the Swin Transformer to capture long-range dependencies and integrates an FPN for robust multi-scale feature aggregation. Its window-based self-attention mechanism also reduces computational complexity, making it well-suited for high-resolution CT images. Additionally, an edge detection module enhances segmentation by providing edge-related features to the decoder, improving boundary precision. A comparative analysis evaluates the EE-FPS-UNet against leading models, including PSPNet, U-Net, Attention U-Net, and DeepLabV3. The results demonstrate that the proposed model outperforms these models, achieving a Dice Similarity of 0.91 and a sensitivity of 0.89, establishing its efficacy for lung nodule segmentation.