Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

OBJECTIVE: Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.

APPROACH: The novel cascaded deep learning models based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix (GLCM) and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.

MAIN RESULTS: We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation all have good performance, with Dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P<0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.

SIGNIFICANCE: The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.