  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.
