Biolab, Department of Electronics, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, Italy.
Comput Methods Programs Biomed. 2010 Dec;100(3):201-21. doi: 10.1016/j.cmpb.2010.04.007. Epub 2010 May 15.
Last 10 years have witnessed the growth of many computer applications for the segmentation of the vessel wall in ultrasound imaging. Epidemiological studies showed that the thickness of the major arteries is an early and effective marker of onset of cardiovascular diseases. Ultrasound imaging, being real-time, economic, reliable, safe, and now seems to become a standard in vascular assessment methodology. This review is an attempt to discuss the most performing methodologies that have been developed so far to perform computer-based segmentation and intima-media thickness (IMT) measurement of the carotid arteries in ultrasound images. First we will present the rationale and the clinical relevance of computer-based measurements in clinical practice, followed by the challenges that one has to face when approaching the segmentation of ultrasound vascular images. The core of the paper is the presentation, discussion, benchmarking and evaluation of different segmentation techniques, including: edge-detection, active contours, dynamic programming, local statistics, Hough transform, statistical modeling, and integration of these approaches. Also, we will discuss and compare the different performance metrics that have been proposed and used to perform the validation. Best performing user-dependent techniques show an average IMT measurement error of about 1μm when compared to human tracings [57], whereas completely automated techniques show errors of about 10μm. The review ends with a discussion about the current standards in carotid wall segmentation and in an overview of the future perspectives, which may include the adoption of advanced and intelligent strategies to let the computer technique measure the IMT in the image portion where measurement is more reliable.
过去 10 年见证了许多用于超声成像中血管壁分割的计算机应用程序的发展。流行病学研究表明，大动脉的厚度是心血管疾病发病的早期和有效标志物。超声成像是实时、经济、可靠、安全的，现在似乎已成为血管评估方法学的标准。本文试图讨论迄今为止开发的性能最佳的方法，以在超声图像中进行基于计算机的颈动脉分割和内膜中层厚度（IMT）测量。首先，我们将介绍基于计算机的测量在临床实践中的基本原理和临床相关性，然后介绍在处理超声血管图像分割时必须面对的挑战。本文的核心是介绍、讨论、基准测试和评估不同的分割技术，包括：边缘检测、主动轮廓、动态规划、局部统计、Hough 变换、统计建模以及这些方法的集成。此外，我们将讨论和比较已经提出并用于验证的不同性能指标。与人工描记相比，表现最佳的用户依赖技术的平均 IMT 测量误差约为 1μm[57]，而完全自动化的技术的误差约为 10μm。本文最后讨论了目前颈动脉壁分割的标准，并概述了未来的展望，其中可能包括采用先进和智能的策略，让计算机技术在测量更可靠的图像部分进行 IMT 测量。
