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Ultrasound is the primary modality to examine fetal growth during pregnancy,while the image quality could be affected by various factors. Qualityassessment is essential for controlling the quality of ultrasound images toguarantee both the perceptual and diagnostic values. Existing automatedapproaches often require heavy structural annotations and the predictions maynot necessarily be consistent with the assessment results by human experts.Furthermore, the overall quality of a scan and the correlation between thequality of frames should not be overlooked. In this work, we propose areinforcement learning framework powered by two hierarchical agents thatcollaboratively learn to perform both frame-level and video-level qualityassessments. It is equipped with a specially-designed reward mechanism thatconsiders temporal dependency among frame quality and only requires sparsebinary annotations to train. Experimental results on a challenging fetal braindataset verify that the proposed framework could perform dual-level qualityassessment and its predictions correlate well with the subjective assessmentresults.

Hierarchical Agent-based Reinforcement Learning Framework for Automated Quality Assessment of Fetal Ultrasound Video