1. Using ResNet to Utilize 4-class T2-FLAIR Slice Classification Based on the Cholinergic Pathways Hyperintensities Scale for Pathological Aging
- Author
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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, and Shih, Yao-Chia
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
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., Comment: 8 pages, 2 figures, 2 tables
- Published
- 2023