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Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach

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.
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https://hal.archives-ouvertes.fr/hal-02962336
Contributor : Karyna Isaieva Connect in order to contact the contributor
Submitted on : Friday, October 9, 2020 - 10:28:07 AM
Last modification on : Friday, July 8, 2022 - 10:08:30 AM

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Karyna Isaieva, Yves Laprie, Nicolas Turpault, Alexis Houssard, Jacques Felblinger, et al.. Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach. Applied Artificial Intelligence, 2020, 34 (14), pp.1115-1123. ⟨10.1080/08839514.2020.1824090⟩. ⟨hal-02962336⟩

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