A method for identifying pleural lines in B-mode ultrasound images-ZENTIME PUBLISHING CORPORATION LIMITED

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A method for identifying pleural lines in B-mode ultrasound images

Tingting Zhou¹, Haozhe Zhuang¹, Shiju Yan¹, Erze Xie¹, Yibo Ma², Tao Zhang¹, Tianxiang Yu¹, Shuang Deng¹

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. 2Department of Ultrasound, the Third Affiliated Hospital of Soochow University, Changzhou 213000, Jiangsu, China.

Address correspondence to: Shiju Yan, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. Email: yanshiju@usst.edu.cn; Yibo Ma, Department of Ultrasound, the Third Affiliated Hospital of Soochow University, No.185 Juqian Street, Changzhou 213000, Jiangsu, China. Email: mayibo@czfph.com.

DOI: https://doi.org/10.61189/594641kmfbkw

Highlights

● Automated 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.

Abstract

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.

Keywords: Pleural line identification, lung ultrasound, pneumothorax, automatic identification algorithm, image processing

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