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首页> 外文期刊>Computer speech and language >A deep neural network based correction scheme for improved air-tissue boundary prediction in real-time magnetic resonance imaging video
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A deep neural network based correction scheme for improved air-tissue boundary prediction in real-time magnetic resonance imaging video

机译：基于深度神经网络基于神经网络的改进的空气组织边界预测实时磁共振成像视频

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摘要

The real-time Magnetic Resonance Imaging (rtMRI) video captures the vocal tract movements in the mid-sagittal plane during speech. Air tissue boundaries (ATBs) are contours that trace the transition between the high-intensity tissue corresponding to the speech artic-ulators and the low-intensity airway cavity in the rtMRI video. The ATB segmentation in an rtMRI video is a common preprocessing step which is used for many speech production and speech processing applications. However, ATB segmentation is very challenging due to the low resolution and low signal-to-noise ratio of the rtMRI images. Several works have been proposed in the literature for accurate ATB segmentation. However, every ATB segmentation technique, be it knowledge-based or data-driven, has its own limitations due to mode! assumption or data quality. The errors in the predicted ATBs from a typical ATB segmentation approach can be corrected in a data-driven manner as a post-processing step. In this work, we propose a deep neural network (DNN) based correction scheme for improving the ATB segmentation. In the DNN based correction approach, the correction of each point on a predicted ATB is done using a pattern of intensity variation in the direction of the normal to the predicted ATB at that point. For this, inputs and target outputs needed for DNN training are generated using a normal-grid based method. Experimental results show that the proposed DNN based correction yields more accurate ATBs in terms of Dynamic Time Warping (DTW) distance compared to the ATB segmentation approaches it is applied on. Thus, the DNN based correction could be used as a post-processing step to improve the accuracy of the predicted ATBs from any segmentation scheme.
机译：实时磁共振成像（RTMRI）视频在语音期间捕获中矢状平面中的声带运动。空气组织界限（ATB）是追踪与RTMRI视频中对应于语音伪装器和低强度气道腔的高强度组织之间的转变的轮廓。 RTMRI视频中的ATB分段是用于许多语音生产和语音处理应用的常见预处理步骤。然而，由于RTMRI图像的低分辨率和低信噪比，ATB分割非常具有挑战性。在文献中提出了几项作品，以获得准确的ATB分段。但是，每个ATB分段技术，都是基于知识的或数据驱动的，因此由于模式而有自己的限制！假设或数据质量。从典型的ATB分段方法中预测的ATB中的错误可以以数据驱动的方式校正作为后处理步骤。在这项工作中，我们提出了一种基于神经网络（DNN）的校正方案，用于改进ATB分段。在基于DNN的校正方法中，使用在该点处的正常方向上的强度变化模式在预测的ATB方向上进行预测ATB上的每个点的校正。为此，使用基于常规网格的方法生成DNN训练所需的输入和目标输出。实验结果表明，基于DNN的校正在动态时间翘曲（DTW）距离方面，与应用接近的ATB分段接近相比，基于DNN的校正产生更准确的ATB。因此，基于DNN的校正可以用作后处理步骤，以提高来自任何分段方案的预测ATB的准确性。

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