Lung ultrasound video scoring using a novel motion-aware segmentation technique: Toward automated neonatal LUS scoring.

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Abstract

Lung ultrasound (LUS) is an essential tool for diagnosing lung diseases. However, its effectiveness is often limited by its reproducibility, making interpretation challenging for clinicians. LUS diagnosis typically relies on subjective assessments of pleural line and vertical artifacts. To address this limitation, we introduce a novel quantitative approach aimed at reducing the need to rely on human operators (HOs) for LUS data assessment (i.e., improving the reproducibility). In the first phase of our study, we propose a hybrid method that integrates motion estimation and K-means clustering for automated segmentation of LUS images. The technique utilizes K-means clustering to identify pleural line based on intensity variations, while motion estimation detects vertical artifacts by analyzing motion vectors between consecutive frames. Rather than employing a conventional learning-based classification model, we develop an interpretable scoring framework that assigns scores to individual video frames according to standard scoring criteria. A threshold-based approach is then applied to aggregate frame-level scores, determining the final score for each video. We evaluated our method on a clinical dataset comprising 420 neonatal LUS videos from 70 patients, with annotations provided by three HOs. When using the majority vote among HOs as the reference standard, our method achieved a video-level accuracy of 0.72. For cases with full agreement among HOs, accuracy improved to 0.77. These results demonstrate that our approach offers comparable or superior performance to state-of-the-art deep learning (DL)-based methods in terms of scoring consistency, while reducing the need for a huge training dataset.

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