Automated quantification of abdominal aortic calcification using 3D nnU-Net: a novel approach to assess AAA rupture risk.

ABSTRACT:

Background

Abdominal aortic aneurysms (AAA) pose a serious rupture risk, heightened by aortic calcification. Traditional calcification scoring methods are slow and require expertise. This study aims to construct a convolutional neural network (nnU-Net) model for automatic quantification and segmentation of abdominal aortic calcification from a single CTA scan.

Methods

This retrospective study included 100 patients who underwent abdominal aortic CTA between January 2018 and October 2023, meeting specific inclusion criteria. Vessel and calcification segmentation were manually scored by two physicians, and an nnU-Net deep learning model was developed to automate calcification measurement. Model performance was assessed using Dice scores. Agreement between manual and model-based

SUBMITTER: Luo YL 

PROVIDER: S-EPMC12403428 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature