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. In this way, the
existing pre-trained large model can be used as an effective warm start for our
network. 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.

本研究提出了一种基于视频分类的自动颈动脉狭窄分级框架，采用弱监督学习和引入 AMDF 转换器编码器进行多维度特征融合，从大规模的颈动脉超声图像视频数据集中有效地区分颈动脉狭窄，优于强基线模型。