Visualization of Organ Movements Using Automatic Region Segmentation of Swallowing CT
Journal:
arXiv
Published Date:
Jan 29, 2025
Abstract
This study presents the first report on the development of an artificial
intelligence (AI) for automatic region segmentation of four-dimensional
computer tomography (4D-CT) images during swallowing. The material consists of
4D-CT images taken during swallowing. Additionally, data for verifying the
practicality of the AI were obtained from 4D-CT images during mastication and
swallowing. The ground truth data for the region segmentation for the AI were
created from five 4D-CT datasets of swallowing. A 3D convolutional model of
nnU-Net was used for the AI. The learning and evaluation method for the AI was
leave-one-out cross-validation. The number of epochs for training the nnU-Net
was 100. The Dice coefficient was used as a metric to assess the AI's region
segmentation accuracy. Regions with a median Dice coefficient of 0.7 or higher
included the bolus, bones, tongue, and soft palate. Regions with a Dice
coefficient below 0.7 included the thyroid cartilage and epiglottis. Factors
that reduced the Dice coefficient included metal artifacts caused by dental
crowns in the bolus and the speed of movement for the thyroid cartilage and
epiglottis. In practical verification of the AI, no significant misrecognition
was observed for facial bones, jaw bones, or the tongue. However, regions such
as the hyoid bone, thyroid cartilage, and epiglottis were not fully delineated
during fast movement. It is expected that future research will improve the
accuracy of the AI's region segmentation, though the risk of misrecognition
will always exist. Therefore, the development of tools for efficiently
correcting the AI's segmentation results is necessary. AI-based visualization
is expected to contribute not only to the deepening of motion analysis of
organs during swallowing but also to improving the accuracy of swallowing CT by
clearly showing the current state of its precision.
