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基于 Radon 变换的超声自动胸膜线检测。

Automated Pleural Line Detection Based on Radon Transform Using Ultrasound.

机构信息

Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China.

Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, China.

出版信息

Ultrason Imaging. 2021 Jan;43(1):19-28. doi: 10.1177/0161734620976408.

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.

摘要

在进行肺部超声检查时,识别胸膜线至关重要,因为胸膜线不仅指示了胸壁和肺部之间的界面,还提供了额外的诊断信息。在当前的临床实践中,胸膜线由临床医生通过视觉检测和评估,这需要经验和技能,对于新手来说具有一定的挑战性。在本研究中,我们开发了一种使用超声自动检测胸膜线的计算机辅助技术。该方法首先利用 Radon 变换检测超声图像中的线对象。然后,根据体质量指数和胸壁厚度的关系,估计胸膜厚度的范围,在此基础上,结合胸膜线的超声特性,检测胸膜线。该方法通过对 21 例气胸患者的 83 组超声数据集进行测试进行了验证。自动化方法成功识别了 76 组数据集中的胸膜线(成功检测率为 91.6%)。在这些成功的案例中,自动方法测量的胸膜线深度与手动测量的深度一致, Bland-Altman 检验也证实了这一点。胸膜线深度的测量误差低于 5%。总之,该方法可以在定义的数据集中自动检测胸膜线。此外,在未来的研究中,对更多不同数据集进行进一步测试后,该方法可能会成为视觉检查的替代方法。

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