Automated Pleural Line Detection Based on Radon Transform Using Ultrasound.

IF 2.5 4区 医学 Q1 ACOUSTICS
Jiangang Chen, Jiawei Li, Chao He, Wenfang Li, Qingli Li
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引用次数: 4

Abstract

It is of vital importance to identify the pleural line when performing lung ultrasound, as the pleural line not only indicates the interface between the chest wall and lung, but offers additional diagnostic information. In the current clinical practice, the pleural line is visually detected and evaluated by clinicians, which requires experiences and skills with challenges for the novice. In this study, we developed a computer-aided technique for automated pleural line detection using ultrasound. The method first utilized the Radon transform to detect line objects in the ultrasound images. The relation of the body mass index and chest wall thickness was then applied to estimate the range of the pleural thickness, based on which the pleural line was detected together with the consideration of the ultrasonic properties of the pleural line. The proposed method was validated by testing 83 ultrasound data sets collected from 21 pneumothorax patients. The pleural lines were successfully identified in 76 data sets by the automated method (successful detection rate 91.6%). In those successful cases, the depths of the pleural lines measured by the automated method agreed with those manually measured as confirmed with the Bland-Altman test. The measurement errors were below 5% in terms of the pleural line depth. As a conclusion, the proposed method could detect the pleural line in an automated manner in the defined data set. In addition, the method may potentially act as an alternative to visual inspection after further tests on more diverse data sets are performed in future studies.

基于氡变换的超声胸膜线自动检测。
在进行肺部超声检查时,胸膜线的识别是至关重要的,因为胸膜线不仅表明胸壁和肺之间的界面,而且提供了额外的诊断信息。在目前的临床实践中,胸膜线是由临床医生目视检测和评估的,这对新手来说需要经验和技能,具有挑战性。在这项研究中,我们开发了一种计算机辅助技术,用于超声自动胸膜线检测。该方法首先利用Radon变换检测超声图像中的线状目标。然后利用身体质量指数与胸壁厚度的关系估计胸膜厚度的范围,在此基础上结合胸膜线的超声特性进行胸膜线的检测。通过对21例气胸患者的83组超声数据集进行测试,验证了该方法的有效性。76个数据集的胸膜线自动识别成功,检出率为91.6%。在这些成功的病例中,自动方法测量的胸膜线深度与人工测量的胸膜线深度一致,并经Bland-Altman试验证实。胸膜线深度测量误差在5%以下。结果表明,该方法能够在定义的数据集中自动检测胸膜线。此外,在未来的研究中,在对更多不同的数据集进行进一步测试后,该方法可能会作为视觉检查的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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