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yanshiju@usst.edu.cn
mayibo@czfph.comAutomated pleural line identification: A method was introduced to automatically identify pleural lines in lung ultrasound images, ensuring diagnosis speed and accuracy.
High reliability: An average of 90.45% identification rate of pleural lines was achieved in a comprehensive experiment on 890 ultrasound videos, highlighting its broad applicability and reliability.
Efficient integration: The algorithm's rapid processing (1.36 seconds for a 5-second video) makes it suitable for seamless integration into ultrasound instrument software, aiding clinicians in diagnosing conditions like pneumo-thorax more efficiently.
Open Accessyanshiju@usst.edu.cnmayibo@czfph.comAutomated pleural line identification: A method was introduced to automatically identify pleural lines in lung ultrasound images, ensuring diagnosis speed and accuracy.
High reliability: An average of 90.45% identification rate of pleural lines was achieved in a comprehensive experiment on 890 ultrasound videos, highlighting its broad applicability and reliability.
Efficient integration: The algorithm's rapid processing (1.36 seconds for a 5-second video) makes it suitable for seamless integration into ultrasound instrument software, aiding clinicians in diagnosing conditions like pneumo-thorax more efficiently.
In ultrasound imaging, the pleura is visualized as echo reflection formed by the echoes of the interface between the pleura and the lung surface. Three major signs of pleura determines whether the patient has pneumothorax. In this paper, we propose a method to identify pleural line for the diagnosis of pneumothorax. Firstly, the gray threshold of ultrasonic image is properly classified by pre-experiment. Secondly, possible pleural line regions are identified based on threshold classification. Thirdly, the region of pleural line is identified based on the known characteristics of pleural line. The last step is to consider whether it is necessary to modify the threshold to accurately identify the pleural line region. Moreover, we tested 890 ultrasound samples, which included three categories: lung sliding, lung point, and lung sliding disappearance. Each category of samples was divided into two subsets, typical and atypical. The average identification rate reached 90.45%. According to the test results, the advantages and disadvantages of the proposed method as well as the further improvement direction were analyzed. This method for identifying pleural line can serve as the groundwork for developing automatic algorithm for diagnosing pneumothorax.
ISSN: 2957-5478
Volume 1, Issue 2
September 2023
Pages: 55-130
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