Towards Multi-Sweep Ultrasound Video Understanding: Application in Detection of Breech Position Using Statistical Priors
Towards Multi-Sweep Ultrasound Video Understanding: Application in Detection of Breech Position Using Statistical Priors
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DOI：
10.1109/isbi53787.2023.10230662
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
A. Gleed;D. Mishra;V. Chandramohan;Z. Fu;A. Self;S. Bhatnagar;Aris T. Papageorghiou;J. Noble
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文献类型：
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作者：
A. Gleed;D. Mishra;V. Chandramohan;Z. Fu;A. Self;S. Bhatnagar;Aris T. Papageorghiou;J. Noble
我们提出了一种新的方式，从多个超声（US）视频扫描创建图形。三节点图对从胎儿US扫描协议获得的三个视频扫描进行建模。节点被分配二进制序列，其表示扫描中所有视频帧上的胎儿头部的帧级检测。我们构建了382个主题级图，并使用图卷积网络将它们用于自动臀位检测。我们使用三个度量对图的边缘进行加权：1）离散Fréchet距离，2）动态时间规整，3）Pearson相关系数。这些度量是根据每个受试者的节点信号计算的。我们发现使用Pearson相关加权图实现了上级性能，超过了基线多层感知器（MLP），实现了28%的分类准确度的提高。最后，我们使用一组独立于382队列的受试者，尝试创建与臀位和头位呈现的整体模式相对应的统计先验图。我们计算这些统计图的边缘的权重，并将每组边缘权重组装成两个模板，分别模拟臀位和头位呈现的不同视频扫描之间的关系。我们发现，在测试过程中使用皮尔逊相关模板的边缘权重增加了14%的分类精度，超过基线MLP。
We propose a novel way of creating graphs from multiple ultrasound (US) video sweeps. A three-node graph models three video sweeps which are obtained from a fetal US sweep protocol. The nodes are assigned binary sequences which represent the frame-level detection of the fetal head across all video frames in a sweep. We build 382 subject-level graphs and use them for automatic breech detection using a graph convolutional network. We experiment with weighting the edges of the graphs using three metrics: 1) discrete Fréchet distance, 2) dynamic time warping, and 3) Pearson correlation coefficient. These metrics are computed from the node signals, per subject. We find superior performance is achieved using Pearson correlation weighted graphs, over a baseline multi-layer perceptron (MLP), achieving an increase in classification accuracy of 28%. Finally, we experiment with creating statistical priors of graphs that correspond to the overall pattern seen for breech and for cephalic presentation, using a set of subjects independent to the 382 cohort. We compute the weights of the edges of these statistical graphs and assemble each set into two templates of edge weights that model the relationship between different video sweeps for breech and cephalic presentation respectively. We find that using the Pearson correlation template of edge weights during testing increases classification accuracy by 14%, over a baseline MLP.