Abstract
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.
Abstract (translated)
从实时磁共振图像中提取舌轮廓是一项非 trivial（复杂）的任务，因为存在以模糊或幽灵状轮廓形式显现的伪影。在本研究中，我们展示了通过 U-Net 自编码卷积神经网络实现自动舌界定的结果。我们同时进行了受试者内和受试者间验证。我们使用了实时磁共振图像，并将手动注释的 1 像素宽的轮廓作为输入。预测的概率图经过后处理以获得 1 像素宽的舌轮廓。结果非常好，略优于已发布的自动舌分割结果。
URL
https://arxiv.org/abs/2412.04893
