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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| Summary: | 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. |
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| ISSN: | 1687-4196 |