Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.05477 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910599521239040 |
|---|---|
| author | Tsai, Wei-Chun Kevin Liu, Yi-Chien Yu, Ming-Chun Chou, Chia-Ju Yan, Sui-Hing Fan, Yang-Teng Huang, Yan-Hsiang Chiu, Yen-Ling Chuang, Yi-Fang Wang, Ran-Zan Shih, Yao-Chia |
| author_facet | Tsai, Wei-Chun Kevin Liu, Yi-Chien Yu, Ming-Chun Chou, Chia-Ju Yan, Sui-Hing Fan, Yang-Teng Huang, Yan-Hsiang Chiu, Yen-Ling Chuang, Yi-Fang Wang, Ran-Zan Shih, Yao-Chia |
| contents | The Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating scale used to assess the extent of cholinergic white matter hyperintensities in T2-FLAIR images, serving as an indicator of dementia severity. However, the manual selection of four specific slices for rating throughout the entire brain is a time-consuming process. Our goal was to develop a deep learning-based model capable of automatically identifying the four slices relevant to CHIPS. To achieve this, we trained a 4-class slice classification model (BSCA) using the ADNI T2-FLAIR dataset (N=150) with the assistance of ResNet. Subsequently, we tested the model's performance on a local dataset (N=30). The results demonstrated the efficacy of our model, with an accuracy of 99.82% and an F1-score of 99.83%. This achievement highlights the potential impact of BSCA as an automatic screening tool, streamlining the selection of four specific T2-FLAIR slices that encompass white matter landmarks along the cholinergic pathways. Clinicians can leverage this tool to assess the risk of clinical dementia development efficiently. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_05477 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Using ResNet to Utilize 4-class T2-FLAIR Slice Classification Based on the Cholinergic Pathways Hyperintensities Scale for Pathological Aging Tsai, Wei-Chun Kevin Liu, Yi-Chien Yu, Ming-Chun Chou, Chia-Ju Yan, Sui-Hing Fan, Yang-Teng Huang, Yan-Hsiang Chiu, Yen-Ling Chuang, Yi-Fang Wang, Ran-Zan Shih, Yao-Chia Image and Video Processing Computer Vision and Pattern Recognition Machine Learning The Cholinergic Pathways Hyperintensities Scale (CHIPS) is a visual rating scale used to assess the extent of cholinergic white matter hyperintensities in T2-FLAIR images, serving as an indicator of dementia severity. However, the manual selection of four specific slices for rating throughout the entire brain is a time-consuming process. Our goal was to develop a deep learning-based model capable of automatically identifying the four slices relevant to CHIPS. To achieve this, we trained a 4-class slice classification model (BSCA) using the ADNI T2-FLAIR dataset (N=150) with the assistance of ResNet. Subsequently, we tested the model's performance on a local dataset (N=30). The results demonstrated the efficacy of our model, with an accuracy of 99.82% and an F1-score of 99.83%. This achievement highlights the potential impact of BSCA as an automatic screening tool, streamlining the selection of four specific T2-FLAIR slices that encompass white matter landmarks along the cholinergic pathways. Clinicians can leverage this tool to assess the risk of clinical dementia development efficiently. |
| title | Using ResNet to Utilize 4-class T2-FLAIR Slice Classification Based on the Cholinergic Pathways Hyperintensities Scale for Pathological Aging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2311.05477 |