Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning
Conclusion: The proposed deep learning–based approach, U-Net, shows promise in improving the accuracy and efficiency of segmenting the cisternal segment of the trigeminal nerve. To the best of our knowledge, this is the first fully automated segmentation method for the trigeminal nerve in anatomic M...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/ijbi/6694599 |
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| _version_ | 1849723104960446464 |
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| author | Li-Ming Hsu Shuai Wang Sheng-Wei Chang Yu-Li Lee Jen-Tsung Yang Ching-Po Lin Yuan-Hsiung Tsai |
| author_facet | Li-Ming Hsu Shuai Wang Sheng-Wei Chang Yu-Li Lee Jen-Tsung Yang Ching-Po Lin Yuan-Hsiung Tsai |
| author_sort | Li-Ming Hsu |
| collection | DOAJ |
| description | Conclusion: The proposed deep learning–based approach, U-Net, shows promise in improving the accuracy and efficiency of segmenting the cisternal segment of the trigeminal nerve. To the best of our knowledge, this is the first fully automated segmentation method for the trigeminal nerve in anatomic MRI, and it has the potential to aid in the diagnosis and treatment of various trigeminal nerve–related disorders, such as TN. |
| format | Article |
| id | doaj-art-470c2f13f75641ab9840bb2f0278f095 |
| institution | DOAJ |
| issn | 1687-4196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-470c2f13f75641ab9840bb2f0278f0952025-08-20T03:11:07ZengWileyInternational Journal of Biomedical Imaging1687-41962025-01-01202510.1155/ijbi/6694599Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep LearningLi-Ming Hsu0Shuai Wang1Sheng-Wei Chang2Yu-Li Lee3Jen-Tsung Yang4Ching-Po Lin5Yuan-Hsiung Tsai6Center for Animal Magnetic Resonance ImagingSchool of CyberspaceDepartment of Diagnostic RadiologyDepartment of Diagnostic RadiologyCollege of MedicineInstitute of NeuroscienceDepartment of Diagnostic RadiologyConclusion: The proposed deep learning–based approach, U-Net, shows promise in improving the accuracy and efficiency of segmenting the cisternal segment of the trigeminal nerve. To the best of our knowledge, this is the first fully automated segmentation method for the trigeminal nerve in anatomic MRI, and it has the potential to aid in the diagnosis and treatment of various trigeminal nerve–related disorders, such as TN.http://dx.doi.org/10.1155/ijbi/6694599 |
| spellingShingle | Li-Ming Hsu Shuai Wang Sheng-Wei Chang Yu-Li Lee Jen-Tsung Yang Ching-Po Lin Yuan-Hsiung Tsai Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning International Journal of Biomedical Imaging |
| title | Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning |
| title_full | Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning |
| title_fullStr | Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning |
| title_full_unstemmed | Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning |
| title_short | Automatic Segmentation of the Cisternal Segment of Trigeminal Nerve on MRI Using Deep Learning |
| title_sort | automatic segmentation of the cisternal segment of trigeminal nerve on mri using deep learning |
| url | http://dx.doi.org/10.1155/ijbi/6694599 |
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