Visualization of Organ Movements Using Automatic Region Segmentation of Swallowing CT
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913670764691456 |
|---|---|
| author | Michiwaki, Yukihiro Kikuchi, Takahiro Ijiri, Takashi Inamoto, Yoko Moriya, Hiroshi Ogawa, Takumi Nakatani, Ryota Masaki, Yuto Otake, Yoshito Sato, Yoshinobu |
| author_facet | Michiwaki, Yukihiro Kikuchi, Takahiro Ijiri, Takashi Inamoto, Yoko Moriya, Hiroshi Ogawa, Takumi Nakatani, Ryota Masaki, Yuto Otake, Yoshito Sato, Yoshinobu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visualization of Organ Movements Using Automatic Region Segmentation of Swallowing CT Michiwaki, Yukihiro Kikuchi, Takahiro Ijiri, Takashi Inamoto, Yoko Moriya, Hiroshi Ogawa, Takumi Nakatani, Ryota Masaki, Yuto Otake, Yoshito Sato, Yoshinobu Image and Video Processing Computer Vision and Pattern Recognition Medical Physics 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. |
| title | Visualization of Organ Movements Using Automatic Region Segmentation of Swallowing CT |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2501.17897 |