Deep Recursive Bayesian Tracking for Fully Automatic Centerline Extraction of Coronary Arteries in CT Images.

Byunghwan Jeon

doi:10.3390/s21186087

Abstract

Extraction of coronary arteries in coronary computed tomography (CT) angiography is a prerequisite for the quantification of coronary lesions. In this study, we propose a tracking method combining a deep convolutional neural network (DNN) and particle filtering method to identify the trajectories from the coronary ostium to each distal end from 3D CT images. The particle filter, as a non-linear approximator, is an appropriate tracking framework for such thin and elongated structures; however, the robust ‘vesselness’ measurement is essential for extracting coronary centerlines. Importantly, we employed the DNN to robustly measure the vesselness using patch images, and we integrated softmax values to the likelihood function in our particle filtering framework. Tangent patches represent cross-sections of coronary arteries of circular shapes. Thus, 2D tangent patches are assumed to include enough features of coronary arteries, and the use of 2D patches significantly reduces computational complexity. Because coronary vasculature has multiple bifurcations, we also modeled a method to detect branching sites by clustering the particle locations. The proposed method is compared with three commercial workstations and two conventional methods from the academic literature.

Highlights

Extraction of coronary arteries in coronary computed tomography angiography (CCTA) is a prerequisite task for the automatic quantification of coronary lesions
We propose a convolutional neural network (CNN)-based stochastic tracking algorithm for the extraction of coronary arteries from 3 to D CCTA, which is inspired by recently introduced methods utilizing a spherical local image patch-based CNN [16] and the adaptive particle filtering method [10]
We proposed a deep CNN with particle filtering method (Deep-PF) for extraction of coronary arteries from CT images

Summary

Introduction

Extraction of coronary arteries in coronary computed tomography angiography (CCTA) is a prerequisite task for the automatic quantification of coronary lesions. The quantification of coronary artery lesions still requires manual annotation by an experienced expert, which becomes a considerable burden both in time and cost. Coronary arteries are represented as a tree structure in a three-dimensional (D) volume image and elongated with an inhomogeneous contrast enhancement on the lesion. Automatic segmentation of coronary arteries in CT images remains a challenge because coronary arteries are elongated and have complex tree shapes. In the literature [1,2], considerable attention was paid to the analysis of curvilinear or vascular structures. Coronary arteries as curvilinear objects have the same characteristics, and the representative characteristics are:

Methods

Results

Conclusion