Department of Electronics, Politecnico di Torino, Torino, Italy.
IEEE Trans Image Process. 2012 Mar;21(3):1211-22. doi: 10.1109/TIP.2011.2169270. Epub 2011 Sep 23.
The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.
本文旨在描述一种新颖的、完全自动化的颈动脉（CA）识别、远（远端）壁分割和内膜中层厚度（IMT）测量技术，这是一种用于心血管疾病风险评估的强大临床工具。完全自动化多分辨率边缘捕捉器（CAMES）的架构由以下两个阶段组成：1）基于多分辨率框架中的尺度空间和统计分类的自动 CA 识别，2）用于远（远端）壁和 IMT 测量的管腔内膜（LI）和中膜外膜（MA）界面的自动分割。我们的 365 个 B 型纵向颈动脉图像数据库来自四个不同机构，涵盖不同的种族背景。地面真实（GT）数据库是来自三个临床专家的平均手动分割。CAMES 与 GT 轮廓的 LI 和 MA 界面的平均距离±标准偏差分别为 0.081±0.099 和 0.082±0.197mm。CAMES 与 GT 的 IMT 测量误差为 0.078±0.112mm。CAMES 与之前开发的基于特征提取和分类器（CALEX）的集成方法的自动技术进行了基准测试。尽管 CAMES 低估了 IMT 值，但与 CALEX 相比，它在 LI 和 MA 界面的分割误差方面有了很大的提高，分别提高了 8%和 42%。CAMES 的整体 IMT 测量偏差相对于 CALEX 提高了 36%。最后，本文证明 CAMES 的卓越值为 95.8%，而 CALEX 为 87.4%。多分辨率 CA 识别和远壁分割的结合，导致了一种自动化、低复杂度、实时和准确的颈动脉 IMT 测量技术。在多民族/多机构数据集上的验证证明了该技术的稳健性，它可以作为一种临床有效的 IMT 测量方法，用于动脉粥样硬化疾病的管理。
