Graph neural network model using radiomics for lung CT image segmentation.
Authors
Affiliations (7)
Affiliations (7)
- Taiyuan University of Technology, College of Computer Science and Technology (College of Data Science), Taiyuan, 030024, Shanxi, China. [email protected].
- Taiyuan University of Technology, College of Computer Science and Technology (College of Data Science), Taiyuan, 030024, Shanxi, China.
- North University of China, School of Software, Taiyuan, Shanxi, China.
- First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
- Taiyuan University of Technology, College of Computer Science and Technology (College of Data Science), Taiyuan, 030024, Shanxi, China. [email protected].
- School of Software, Taiyuan University of Technology, Taiyuan, Shanxi, China. [email protected].
- Jinzhong College of Information, College of Information, Jinzhong, Shanxi, China. [email protected].
Abstract
Early detection of lung cancer is critical for improving treatment outcomes, and automatic lung image segmentation plays a key role in diagnosing lung-related diseases such as cancer, COVID-19, and respiratory disorders. Challenges include overlapping anatomical structures, complex pixel-level feature fusion, and intricate morphology of lung tissues all of which impede segmentation accuracy. To address these issues, this paper introduces GEANet, a novel framework for lung segmentation in CT images. GEANet utilizes an encoder-decoder architecture enriched with radiomics-derived features. Additionally, it incorporates Graph Neural Network (GNN) modules to effectively capture the complex heterogeneity of tumors. Additionally, a boundary refinement module is incorporated to improve image reconstruction and boundary delineation accuracy. The framework utilizes a hybrid loss function combining Focal Loss and IoU Loss to address class imbalance and enhance segmentation robustness. Experimental results on benchmark datasets demonstrate that GEANet outperforms eight state-of-the-art methods across various metrics, achieving superior segmentation accuracy while maintaining computational efficiency.