Dynamic Local Conformal Reinforcement Network (DLCR) for Aortic Dissection Centerline Tracking.

Abstract

Pre-extracted aortic dissection (AD) centerline is very useful for quantitative diagnosis and treatment of AD disease. However, centerline extraction is challenging because (i) the lumen of AD is very narrow and irregular, yielding failure in feature extraction and interrupted topology; and (ii) the acute nature of AD requires a quick algorithm, however, AD scans usually contain thousands of slices, centerline extraction is very time-consuming. In this paper, a fast AD centerline extraction algorithm, which is based on a local conformal deep reinforced agent and dynamic tracking framework, is presented. The potential dependence of adjacent center points is utilized to form the novel 2.5D state and locally constrains the shape of the centerline, which improves overlap ratio and accuracy of the tracked path. Moreover, we dynamically modify the width and direction of the detection window to focus on vessel-relevant regions and improve the ability in tracking small vessels. On a public AD dataset that involves 100 CTA scans, the proposed method obtains average overlap of 97.23% and mean distance error of 1.28 voxels, which outperforms four state-of-the-art AD centerline extraction methods. The proposed algorithm is very fast with average processing time of 9.54s, indicating that this method is very suitable for clinical practice.