Transplant-ready? Evaluating AI lung segmentation models in candidates with severe lung disease

Purpose: This study evaluates publicly available deep-learning-based lung segmentation models in transplant-eligible patients to determine their performance across disease severity levels, pathology categories, and lung sides, and to identify limitations impacting their use in preoperative planning in lung transplantation. Approach: This retrospective study included 32 patients who underwent chest CT scans at Duke University Health System between 2017 and 2019 (a total of 3645 2D axial slices). Patients with standard axial CT scans were selected based on the presence of two or more lung pathologies of varying severity. Lung segmentation was performed using three previously developed deep learning models: Unet-R231, TotalSegmentator, and MedSAM. Performance was assessed using quantitative metrics (volumetric similarity, Dice similarity coefficient, and Hausdorff distance) and a qualitative measure (four-point clinical acceptability scale). Results: ), without significant differences among lung sides or pathology types. Conclusions: Unet-R231 provided the most accurate automated lung segmentation among evaluated models, with TotalSegmentator being a close second, although their performance declined significantly in moderate-to-severe cases, emphasizing the need for specialized model fine-tuning in severe pathology contexts.