Transformer-Based Lung CT Image Segmentation

Transformer-based models have emerged as a breakthrough in lung CT image segmentation, offering superior performance over traditional CNNs by effectively capturing long-range dependencies through self-attention mechanisms. These models excel in segmenting complex lung structures and pathologies, including pulmonary nodules, COVID-19 infection regions, and ground-glass opacities, demonstrating particular robustness in analyzing scattered lesions that challenge conventional approaches. Current implementations primarily follow two paradigms: hybrid architectures that integrate CNN's local feature extraction with Transformer's global modeling (e.g., TransUNet, Swin UNETR), and pure Transformer models (e.g., UNETR, ViT), both achieving state-of-the-art results on benchmark datasets like LUNA16 and MosMed. Despite these advances, several challenges persist, including high computational demands, dependence on large annotated datasets, and occasional limitations in capturing fine local details. Researchers are addressing these issues through innovations in sparse attention mechanisms, efficient model architectures, and data augmentation techniques. Looking ahead, key development areas include lightweight model deployment for clinical settings (e.g., MobileViT adaptations), multimodal fusion strategies combining CT with other imaging modalities, and privacy-preserving federated learning approaches. As 3D Transformer architectures mature and incorporate more domain-specific knowledge, they are poised to transform clinical applications ranging from early lung cancer detection to quantitative COVID-19 assessment. However, achieving optimal balance between computational efficiency, segmentation accuracy, and clinical interpretability remains an ongoing challenge that will shape future research directions in this rapidly evolving field.