Automatic detection of A‐line in lung ultrasound images using deep learning and image processing. Issue 1 (23rd August 2022) - Xing, Wenyu; Li, Guannan; He, Chao; Huang, Qiming; Cui, Xulei; Li, Qingli; Li, Wenfang; Chen, Jiangang; Ta, Dean
Abstract: Background: Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A‐line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A‐line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A‐line in LUS image is important. Purpose: In order to solve the disadvantages of traditional A‐line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A‐line detection method for LUS images with different probe types in this paper. Methods: First, the improved Faster R‐CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image‐processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A‐line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five‐fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image‐processing‐based A‐line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. Results: After 100 epochs, the mean loss value of training and validation set of improved Faster R‐CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R‐CNN model. The accuracy, sensitivity, and specificity of A‐line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two‐stage experiment (pleural line localization and A‐line detection) was calculated as the final accuracy of the whole A‐line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. Conclusions: The proposed method combining image processing and deep learning can automatically and accurately detect A‐line in LUS images with different probe types, which has important application value for clinical diagnosis.