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.