论文标题
部分可观测时空混沌系统的无模型预测
An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production
论文作者
论文摘要
众所周知，在语音生产中实时磁共振成像（RTMRI）视频的空气组织边界（ATB）分割中的最佳性能是通过三维卷积神经网络（3D-CNN）模型实现的。但是，对该模型的评估以及文献中报告的其他ATB分割技术是使用整个原始和预测轮廓之间的动态时间翘曲（DTW）距离进行的。这样的评估措施可能无法捕获预测轮廓中的局部错误。对预测轮廓的仔细分析揭示了诸如Contour1（ATB）（ATB）（由上唇，硬pa和粘膜组成的ATB）和Contour2（ATB覆盖下颌，下唇，舌底座和epiglottis）等区域的错误，这些区域在全球评估中未捕获。在这项工作中，我们会自动检测到此类错误，并提出针对同一错误的校正方案。我们还建议在Contour1和Contour2中分别针对ATB分割的两个新评估指标，以明确捕获这些轮廓中的两种错误。提出的检测和校正策略导致CORTOUR1的这两个评估指标的提高61.8％和61.4％，以及Contour2的67.8％和28.4％。另一方面，传统的DTW距离提高了44.6％的轮廓，以及Contour2的4.0％。
The best performance in Air-tissue boundary (ATB) segmentation of real-time Magnetic Resonance Imaging (rtMRI) videos in speech production is known to be achieved by a 3-dimensional convolutional neural network (3D-CNN) model. However, the evaluation of this model, as well as other ATB segmentation techniques reported in the literature, is done using Dynamic Time Warping (DTW) distance between the entire original and predicted contours. Such an evaluation measure may not capture local errors in the predicted contour. Careful analysis of predicted contours reveals errors in regions like the velum part of contour1 (ATB comprising of upper lip, hard palate, and velum) and tongue base section of contour2 (ATB covering jawline, lower lip, tongue base, and epiglottis), which are not captured in a global evaluation metric like DTW distance. In this work, we automatically detect such errors and propose a correction scheme for the same. We also propose two new evaluation metrics for ATB segmentation separately in contour1 and contour2 to explicitly capture two types of errors in these contours. The proposed detection and correction strategies result in an improvement of these two evaluation metrics by 61.8% and 61.4% for contour1 and by 67.8% and 28.4% for contour2. Traditional DTW distance, on the other hand, improves by 44.6% for contour1 and 4.0% for contour2.
