Multi-centre Validation of Automated Outer-to-Outer Wall Segmentation of Infrarenal Abdominal Aortic Aneurysms Using a nnU-Net Based Model.

Accurate outer-to-outer wall aortic segmentation is essential for abdominal aortic aneurysm (AAA) assessment and endovascular aneurysm repair (EVAR) planning, yet manual segmentation is time-consuming and impractical for routine use. This study aimed to validate a pre-trained open-source nnU-Net-based model for fully automated outer-to-outer wall segmentation of the abdominal aorta in AAA patients. In this retrospective multicentre observational validation study, 75 consecutive AAA patients (25 per centre) from three Dutch hospitals were included. Manual segmentations of pre-EVAR CTA scans were performed. A second observer independently segmented 30 cases to assess interobserver variability. Manual and automatic segmentations were compared using the Dice similarity coefficient (DSC), Jaccard index, maximum and 95th percentile Hausdorff distances (HD max, HD95), mean surface distance, and absolute and relative volume difference. Agreement was assessed for the full abdominal aorta and the infrarenal neck separately. Automatic segmentation achieved excellent agreement with manual segmentations, with a median DSC of 0.97 (IQR 0.96-0.97) and median HD95 of 5.5 (IQR 3.8-7.6) mm. The mean volume bias was 2.06 ml. Performance was comparable to interobserver variability between manual segmentations (median DSC 0.96 [IQR 0.95-0.97]). For the infrarenal neck, a median DSC of 0.95 (IQR 0.93-0.96) was achieved. Automatic segmentation required less than one minute per scan on a dedicated GPU, compared to 45-60 minutes for manual segmentation. Automated outer-to-outer wall aortic segmentation using a pre-trained nnU-Net-based model achieves excellent agreement with manual segmentations, comparable to interobserver variability, across a consecutive multicentre dataset. This approach may support more reproducible and efficient AAA analysis in clinical and research settings.