AI-Assisted Lung Segmentation for Lung Transplant Volumetry: Effect of Radiation Dose Reduction

OBJECTIVE: Accurate lung volumetry is essential for donor-recipient size matching in lung transplantation. This study evaluated the impact of radiation dose reduction on the robustness of AI-based whole-lung segmentation, 5-lobe segmentation, and automated tissue classification. METHODS: Fifty cases from the Cancer Image Archive (TCIA) Low-dose CT Image and Projection Data set were analyzed. A reduced-dose data set at 10% of the routine dose for each case was generated using validated projection-domain noise insertion. Whole-lung and 5-lobe segmentation were performed using the Lung CT Segmenter module with the lungmask deep learning model in 3D Slicer. Segmentation accuracy was quantified using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and maximum Hausdorff distance (HDmax). Automated tissue classification and volumetry were performed using the Lung CT Analyzer module. RESULTS: Whole-lung segmentation demonstrated excellent consistency between full-dose and 10% dose images (mean DSC 0.997±0.002, HD95 0.73±0.26 mm). In contrast, 5-lobe segmentation showed substantially lower accuracy and greater variability, even at routine dose. Automated tissue classification revealed a mean decrease in functional (inflated) lung volume of 17.12%±5.69% in the right lung and 17.15%±5.56% in the left lung, accompanied by marked increases in emphysema, infiltrated, and collapsed volumes. CONCLUSION: AI-based whole-lung segmentation remains highly accurate at 10% of the routine dose, supporting its potential for lung transplant volumetry. However, tissue classification and 5-lobe segmentation exhibited notable degradation, highlighting limitations of existing tools at ultra-low dose levels.