Inflated 3D Convolution-Transformer for Weakly-supervised Carotid Stenosis Grading with Ultrasound Videos

Localization of the narrowest position of the vessel and corresponding vessel and remnant vessel delineation in carotid ultrasound (US) are essential for carotid stenosis grading (CSG) in clinical practice. However, the pipeline is time-consuming and tough due to the ambiguous boundaries of plaque and temporal variation. To automatize this procedure, a large number of manual delineations are usually required, which is not only laborious but also not reliable given the annotation difficulty. In this study, we present the first video classification framework for automatic CSG. Our contribution is three-fold. First, to avoid the requirement of laborious and unreliable annotation, we propose a novel and effective video classification network for weakly-supervised CSG. Second, to ease the model training, we adopt an inflation strategy for the network, where pre-trained 2D convolution weights can be adapted into the 3D counterpart in our network for an effective warm start. Third, to enhance the feature discrimination of the video, we propose a novel attention-guided multi-dimension fusion (AMDF) transformer encoder to model and integrate global dependencies within and across spatial and temporal dimensions, where two lightweight cross-dimensional attention mechanisms are designed. Our approach is extensively validated on a large clinically collected carotid US video dataset, demonstrating state-of-the-art performance compared with strong competitors.

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

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.