Deep Learning-Based Segmentation of Pleural Effusion from Ultrasound Using Coordinate Convolutions

Abstract

Ultrasound imaging plays a crucial role in assessing disease and making diagnoses for a range of conditions, especially so in low-to-middle-income (LMIC) countries. One such application is the assessment of pleural effusion, which can be associated with multiple morbidities including tuberculosis (TB). Currently, assessment of pleural effusion is performed manually by the sonographer during the ultrasound examination, leading to significant intra-/inter-observer variability. In this work, we investigate the use of deep learning (DL) to automate the process of pleural effusion segmentation from ultrasound images. On two ultrasound datasets of suspected TB patients acquired in a LMIC setting, we achieve median Dice Similarity Coefficients (DSCs) of 0.82 and 0.74 respectively using the nnU-net DL model. We also investigate the use of coordinate convolutions in the DL model and find that this results in a statistically significant improvement in the median DSC on the first dataset to 0.85, with no significant change on the second dataset. This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound imaging and paves the way for future work on artificial intelligence-assisted acquisition and interpretation of ultrasound images. This could enable accurate and robust assessment of pleural effusion in LMIC settings where there is often a lack of experienced radiologists to perform such assessments.