MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis

• URL: http://arxiv.org/abs/2507.15340v1
• Date: Mon, 21 Jul 2025 07:53:49 GMT
• Title: MedSR-Impact: Transformer-Based Super-Resolution for Lung CT Segmentation, Radiomics, Classification, and Prognosis
• Authors: Marc Boubnovski Martell, Kristofer Linton-Reid, Mitchell Chen, Sumeet Hindocha, Benjamin Hunter, Marco A. Calzado, Richard Lee, Joram M. Posma, Eric O. Aboagye,
• Abstract summary: High-resolution computed tomography (CT) is essential for accurate diagnosis and treatment planning in thoracic diseases.<n>We present the Transformer Volumetric Super-Resolution Network (textbfTVSRN-V2), a transformer-based super-resolution framework for clinical lung CT analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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