Using ResNet to Utilize 4-class T2-FLAIR Slice Classification Based on
  the Cholinergic Pathways Hyperintensities Scale for Pathological Aging
Wei-Chun Kevin Tsai Yi-Chien Liu Ming-Chun Yu Chia-Ju Chou Sui-Hing Yan Yang-Teng Fan Yan-Hsiang Huang Yen-Ling Chiu Yi-Fang Chuang Ran-Zan Wang Yao-Chia Shih
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.