使用卷积神经网络自动检测肺部超声中的胸膜线和肺滑动。
Automatic detection of pleural line and lung sliding in lung ultrasonography using convolutional neural networks.
作者信息
Uchida Takeyoshi, Tanaka Yukimi, Suzuki Akihiro
机构信息
Material Strength Standards Group, Research Institute for Engineering Measurement, National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology, Central 3, 1-1-1 Umezono, Tsukuba, 305-8563, Japan.
Department of Anesthesiology and Critical Care Medicine, Jichi Medical University, Yakushiji, Shimotsuke-shi, Tochigi, 329-0498, Japan.
出版信息
Heliyon. 2024 Jul 24;10(15):e34700. doi: 10.1016/j.heliyon.2024.e34700. eCollection 2024 Aug 15.
BACKGROUND
Lung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis.
METHODS
We propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax.
RESULTS
Our proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions.
背景
肺部超声检查（LUS）是一种有价值的诊断工具，但该领域缺乏具有丰富知识和重要经验的LUS专家。卷积神经网络（CNN）有潜力通过促进计算机辅助诊断来缓解这一问题。
方法
我们提出了一种基于CNN方法的LUS诊断计算机辅助系统。首先，我们研究了胸膜线和肺滑动。胸膜线指示超声图像中胸膜的位置，在首次确认胸膜线位置后进行LUS检查。肺滑动定义为胸膜线的移动，该特征的缺失与气胸相关。
结果
我们提出的方法准确地检测到了胸膜线和肺滑动，证明了其提供有关肺部病变有价值诊断信息的潜力。
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