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Combining quantitative and qualitative knowledge for scoring pleural line in lung ultrasound
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  • Wenyu Xing ,
  • Chao He ,
  • Yebo Ma ,
  • Yiman Liu ,
  • Qingli Li ,
  • Wenfang Li ,
  • Jiangang Chen ,
  • Dean Ta
Wenyu Xing
Fudan University, Fudan University, Fudan University

Corresponding Author:[email protected]

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Yiman Liu
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Qingli Li
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Wenfang Li
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Jiangang Chen
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With the development of lung diseases and the wide application of lung ultrasound (LUS), the independent analysis of various indicators in LUS images are of great significance in clinic. In this paper, we proposed a quantitative and qualitative method for extracting, analyzing, and scoring pleural lines with different lesions in LUS images. The extraction module was composed of the customized cascaded localization and segmentation models based on convolution and multilayer perceptron. The analysis module used eight textural and three morphological parameters to quantitatively analyze the features of two different output images from the localization and segmentation models, respectively. The scoring module adopted four supervised machine learning classifiers including support vector machine, k-nearest neighbor, random forest, and decision tree to qualitatively evaluate pleural lines with different severities in LUS images. The experiments were performed on the 5390 LUS images (i.e., segmentation: 1620, scoring: 3770) acquired from Coronavirus Disease 2019 pneumonia patients with convex ultrasound probes. Experimental results showed that the proposed line extraction method can accurately achieve the localization and segmentation of pleural lines. The support vector machine classifier with combining texture and morphological features as input achieved optimal scoring performance with the accuracy, sensitivity, specificity, F1 score, and AUC being 94.47%, 97.31%, 94.50%, 0.9457, and 0.9822, respectively. Compared with other models, it also proves that the proposed method has better scoring performance. Thus, the proposed method has great application potential for clinical application.