A robust construction algorithm of the centerline skeleton for complex aortic vascular structure using computational fluid dynamics

A stable and quantitative method for dimensionality reduction of aortic centerline

Aortic dissection (AD) is a fatal aortic disease with high mortality. Assessing the morphology of the aorta is critical for diagnostic and surgical decisions. Aortic centerline projection methods have been used to evaluate the morphology of the aorta. However, there is a big difference between the current model of primary plane projection (PPP) and the actual shape of individuals, which is not conducive to morphological statistical analysis. Finding a method to compress the three-dimensional information of the aorta into two dimensions is helpful to clinical decision-making. In this paper, the evaluation parameters, including contour length (CL), enclosure area, and the sum of absolute residuals (SAR), were introduced to objectively evaluate the optimal projection plane rather than artificial subjective judgment. Our results showed that the optimal projection plane could be objectively characterized by the three evaluation parameters. As the morphological criterion, SAR is optimal among the three parameters. Compared to the optimal projection plane selected by traditional PPP, our method has better AD discrimination in the analysis of aortic tortuosity, and is conducive to the clinical operation of AD. Thus, it has application prospects for the preprocessing techniques for the geometric morphology analysis of AD.

Automatic measurement of maximal diameter of abdominal aortic aneurysm on computed tomography angiography using artificial intelligence

INTRODUCTION

The treatment of abdominal aortic aneurysm (AAA) relies on surgical repair and the indication mainly depends on its size evaluated by the maximal diameter (Dmax). The aim of this study was to evaluate a new automatic method based on artificial intelligence (AI) to measure the Dmax on computed tomography angiography (CTA).

METHODS

A fully automatic segmentation of the vascular system was performed using a hybrid method combining expert system with supervised deep learning (DL). The aorta centreline was extracted from the segmented aorta and the aortic diameters were automatically calculated. Results were compared to manual segmentation performed by two human operators.

RESULTS

The median absolute error between the two human operators was 1.2 mm (IQR 0.5- 1.9). The automatic method using the DL algorithm demonstrated correlation with the human segmentation, with a median absolute error of 0.8 (0.5- 4.2) mm and a coefficient correlation of 0.91 (p<0.001).

CONCLUSION

Although validation in larger cohorts is required, this method brings perspectives to develop new tools to standardize and automate the measurement of AAA Dmax in order to help clinicians in the decision-making process.

Algorithm of Pulmonary Vascular Segment and Centerline Extraction

Because pulmonary vascular lesions are harmful to the human body and difficult to detect, computer-assisted diagnosis of pulmonary blood vessels has become the focus and difficulty of the current research. An algorithm of pulmonary vascular segment and centerline extraction which is consistent with the physician's diagnosis process is proposed for the first time. We construct the projection of maximum density, restore the vascular space information, and correct random walk algorithm to satisfy automatic and accurate segmentation of blood vessels. Construct a local 3D model to restrain Hessian matrix when extracting centerline. In order to assist the physician to make a correct diagnosis and verify the effectiveness of the algorithm, we proposed a visual expansion model. According to the 420 high-resolution CT data of lung blood vessels labeled by physicians, the accuracy of segmentation algorithm AOM reached 93%, and the processing speed was 0.05 s/frame, which achieved the clinical application standards.