Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15340 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916853030322176 |
|---|---|
| author | Martell, Marc Boubnovski Linton-Reid, Kristofer Chen, Mitchell Hindocha, Sumeet Hunter, Benjamin Calzado, Marco A. Lee, Richard Posma, Joram M. Aboagye, Eric O. |
| author_facet | Martell, Marc Boubnovski Linton-Reid, Kristofer Chen, Mitchell Hindocha, Sumeet Hunter, Benjamin Calzado, Marco A. Lee, Richard Posma, Joram M. Aboagye, Eric O. |
| contents | High-resolution volumetric computed tomography (CT) is essential for accurate diagnosis and treatment planning in thoracic diseases; however, it is limited by radiation dose and hardware costs. We present the Transformer Volumetric Super-Resolution Network (\textbf{TVSRN-V2}), a transformer-based super-resolution (SR) framework designed for practical deployment in clinical lung CT analysis. Built from scalable components, including Through-Plane Attention Blocks (TAB) and Swin Transformer V2 -- our model effectively reconstructs fine anatomical details in low-dose CT volumes and integrates seamlessly with downstream analysis pipelines. We evaluate its effectiveness on three critical lung cancer tasks -- lobe segmentation, radiomics, and prognosis -- across multiple clinical cohorts. To enhance robustness across variable acquisition protocols, we introduce pseudo-low-resolution augmentation, simulating scanner diversity without requiring private data. TVSRN-V2 demonstrates a significant improvement in segmentation accuracy (+4\% Dice), higher radiomic feature reproducibility, and enhanced predictive performance (+0.06 C-index and AUC). These results indicate that SR-driven recovery of structural detail significantly enhances clinical decision support, positioning TVSRN-V2 as a well-engineered, clinically viable system for dose-efficient imaging and quantitative analysis in real-world CT workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis Martell, Marc Boubnovski Linton-Reid, Kristofer Chen, Mitchell Hindocha, Sumeet Hunter, Benjamin Calzado, Marco A. Lee, Richard Posma, Joram M. Aboagye, Eric O. Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition High-resolution volumetric computed tomography (CT) is essential for accurate diagnosis and treatment planning in thoracic diseases; however, it is limited by radiation dose and hardware costs. We present the Transformer Volumetric Super-Resolution Network (\textbf{TVSRN-V2}), a transformer-based super-resolution (SR) framework designed for practical deployment in clinical lung CT analysis. Built from scalable components, including Through-Plane Attention Blocks (TAB) and Swin Transformer V2 -- our model effectively reconstructs fine anatomical details in low-dose CT volumes and integrates seamlessly with downstream analysis pipelines. We evaluate its effectiveness on three critical lung cancer tasks -- lobe segmentation, radiomics, and prognosis -- across multiple clinical cohorts. To enhance robustness across variable acquisition protocols, we introduce pseudo-low-resolution augmentation, simulating scanner diversity without requiring private data. TVSRN-V2 demonstrates a significant improvement in segmentation accuracy (+4\% Dice), higher radiomic feature reproducibility, and enhanced predictive performance (+0.06 C-index and AUC). These results indicate that SR-driven recovery of structural detail significantly enhances clinical decision support, positioning TVSRN-V2 as a well-engineered, clinically viable system for dose-efficient imaging and quantitative analysis in real-world CT workflows. |
| title | MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.15340 |