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A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound.

机译:关于颈内膜超声的内膜中层厚度(IMT)测量和壁分割技术的最新技术回顾。

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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 1mum when compared to human tracings [57], whereas completely automated techniques show errors of about 10mum. 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.
机译:最近十年见证了许多计算机应用在超声成像中分割血管壁的应用。流行病学研究表明,主要动脉的粗细是心血管疾病发作的早期有效标记。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的标准。这项审查是试图讨论迄今为止执行的最高性能的方法,以执行超声图像中颈动脉的基于计算机的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,然后介绍在进行超声血管图像分割时必须面对的挑战。本文的核心是各种分割技术的介绍,讨论,基准测试和评估,包括:边缘检测,活动轮廓,动态编程,局部统计,霍夫变换,统计建模以及这些方法的集成。此外,我们将讨论并比较已提出并用于执行验证的不同性能指标。与人类追踪相比,性能最佳的用户相关技术显示的平均IMT测量误差约为1mum [57],而全自动技术显示的误差约为10mum。审查以关于颈动脉壁分割的当前标准的讨论以及对未来观点的概述作为结束,其中可能包括采用先进的智能策略,以使计算机技术可以在图像部分测量IMT,从而使测量更为可靠。

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