Bibliographic Details
| Title: |
Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics |
| Authors: |
Lei Pan, Xuechun Wang, Xiuhong Ge, Haiqi Ye, Xiaofen Zhu, Qi Feng, Haibin Wang, Feng Shi, Zhongxiang Ding |
| Source: |
BMC Medical Imaging, Vol 24, Iss 1, Pp 1-9 (2024) |
| Publisher Information: |
BMC, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Medical technology |
| Subject Terms: |
Classic trigeminal neuralgia, Deep learning, Radiomics, Neuroimaging, Medical technology, R855-855.5 |
| More Details: |
Abstract Background This study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals. Methods A total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation. Results On the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1471-2342 |
| Relation: |
https://doaj.org/toc/1471-2342 |
| DOI: |
10.1186/s12880-024-01424-z |
| Access URL: |
https://doaj.org/article/81864199fcaf4dd8865d0083cb9e6a94 |
| Accession Number: |
edsdoj.81864199fcaf4dd8865d0083cb9e6a94 |
| Database: |
Directory of Open Access Journals |
