Automated Detection and Segmentation of Aortoiliac Calcified Plaques Using nnU-Net for Whole-Torso Atherosclerotic...

Cardiovascular disease (CVD) is closely associated with aortoiliac plaque burden, yet current research on its automated detection and segmentation has largely focused on plaque burden analysis using CT angiography. In this study, we present an automated method for aortoiliac plaque detection and segmentation that enables accurate quantification of calcified plaque burden on both non-contrast and contrast-enhanced CT scans.

The training data included 119 non-contrast whole-body PET-CT scans and 23 contrast-enhanced abdominopelvic CT urography scans, all obtained from our institution. The testing data comprised 99 contrast-enhanced thoracoabdominopelvic CT scans from the sarcopenia dataset; 93 from the prostate cancer dataset; 1214 paired non-contrast and contrast-enhanced abdominal CT scans from a renal donor cohort; 9199 non-contrast abdominal CT colonography scans from a second institution; and 1446 non-contrast chest CT scans from a third institution. The nnU-Net was used to train a model for aortoiliac plaque detection and segmentation. Detection accuracy was evaluated on non-contrast chest CT scans. Segmentation accuracy was assessed on CT scans with manually labeled plaque regions from the sarcopenia, prostate, renal donor, and CT colonography datasets. The correlation between Agatston scores on paired non-contrast and contrast-enhanced scans was evaluated in the renal donor cohort. Correlations between whole-torso calcified plaque burden (Agatston scores), demographics, and diseases were analyzed using multivariable analysis on the CT colonography dataset.

Aortoiliac plaques were detected with 88.1% precision, 99.5% recall, and a 93.4% F1 score. Segmentation achieved Dice scores of 64.3-83.7% across two internal contrast-enhanced and two external non-contrast CT datasets, outperforming baseline methods by over 10% (p < 0.001). Agatston scores from paired CT scans showed strong correlation (R² = 0.99). Multivariate analysis showed calcified plaque burden assessment correlated with sex, age, BMI, and smoking (all p < 0.001), as well as alcohol abuse (p = 0.01). The calcified burden assessment was also correlated with CVD, heart failure, myocardial infarction (all p < 0.001), and type 2 diabetes (p = 0.03), but showed no correlation with cancer (p = 0.14) or femoral neck fracture (p = 0.61).

Automated aortoiliac plaque detection enables accurate whole-torso atherosclerotic calcified burden assessment, offering a potential pathway for improved CVD diagnosis and treatment.