用U-Net自动分割MRI中的舌部轮廓，效果优于已有方法。
Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach
- 采用U-Net结构的卷积神经网络进行自动轮廓提取
- 在跨被试和同被试验证中均达到优秀分割效果
- 适合语音与吞咽研究中快速获取舌部图像数据
由于实时磁共振图像中存在模糊或伪影导致的鬼影轮廓，舌部轮廓提取是一项非平凡任务。本文利用U-Net自编码器卷积神经网络实现了自动舌部轮廓分割。采用实时磁共振图像并以人工标注的1像素宽轮廓作为输入，通过后处理预测的概率图获得1像素宽的舌部轮廓。结果表现优异，略优于已发表的自动舌部分割方法。
原文摘要 · Abstract (English)
Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In this work, we present results of automatic tongue delineation achieved by means of U-Net auto-encoder convolutional neural network. We present both intra- and inter-subject validation. We used real-time magnetic resonance images and manually annotated 1-pixel wide contours as inputs. Predicted probability maps were post-processed in order to obtain 1-pixel wide tongue contours. The results are very good and slightly outperform published results on automatic tongue segmentation.
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