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.
@article{arxiv.2412.04893,
title = {Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach},
author = {Karyna Isaieva and Yves Laprie and Nicolas Turpault and Alexis Houssard and Jacques Felblinger and Pierre-André Vuissoz},
journal= {arXiv preprint arXiv:2412.04893},
year = {2024}
}