Hierarchical Agent-based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Video
This work addresses the need for reliable automated quality assessment in fetal ultrasound imaging, which is crucial for ensuring diagnostic accuracy, but it is incremental as it builds on existing reinforcement learning methods with a novel hierarchical approach.
The paper tackled the problem of automated quality assessment for fetal ultrasound videos by proposing a hierarchical agent-based reinforcement learning framework that performs both frame-level and video-level assessments, achieving predictions that correlate well with human expert evaluations on a challenging fetal brain dataset.
Ultrasound is the primary modality to examine fetal growth during pregnancy, while the image quality could be affected by various factors. Quality assessment is essential for controlling the quality of ultrasound images to guarantee both the perceptual and diagnostic values. Existing automated approaches often require heavy structural annotations and the predictions may not necessarily be consistent with the assessment results by human experts. Furthermore, the overall quality of a scan and the correlation between the quality of frames should not be overlooked. In this work, we propose a reinforcement learning framework powered by two hierarchical agents that collaboratively learn to perform both frame-level and video-level quality assessments. It is equipped with a specially-designed reward mechanism that considers temporal dependency among frame quality and only requires sparse binary annotations to train. Experimental results on a challenging fetal brain dataset verify that the proposed framework could perform dual-level quality assessment and its predictions correlate well with the subjective assessment results.