Scholarlink

<p>Alzheimer’s disease (AD) is characterized by a progressive spread of neurofibrillary tangles (NFT), beginning in the medial perirhinal cortex (mPRC), advancing to the entorhinal cortex (ERC), and subsequently involving the hippocampus, lateral perirhinal cortex (lPRC), and the rest of the brain. Given the close relationship between NFT accumulation and neuronal loss, the mPRC reflects a promising structural marker for early diagnosis of AD. However, only limited tools that automatically measure the cortical thickness of the mPRC are currently available. Utilizing the nnU-Net framework, we trained models on structural MRI of 126 adults, with manually segmented labels as ground truth. These models were then applied to an independent dataset of 103 adults (comprising patients with Alzheimer’s dementia, amnestic mild cognitive impairment (aMCI), and healthy controls). High agreement was observed between manual and automated measurements of cortical thickness. Furthermore, we found significant atrophy in the Alzheimer’s dementia group in the mPRC, ERC, and lPRC compared to healthy controls. Comparison of the aMCI group and healthy controls revealed significant differences in the ERC only. The results underscore the utility of our automated segmentation tool in advancing Alzheimer’s research.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Automated segmentation for cortical thickness of the medial perirhinal cortex

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月28日

查看全文
  • Nicolas A. Henzen,
  • Ahmed Abdulkadir,
  • Julia Reinhardt,
  • Maria Blatow,
  • Reto W. Kressig,
  • Sabine Krumm

摘要

Alzheimer’s disease (AD) is characterized by a progressive spread of neurofibrillary tangles (NFT), beginning in the medial perirhinal cortex (mPRC), advancing to the entorhinal cortex (ERC), and subsequently involving the hippocampus, lateral perirhinal cortex (lPRC), and the rest of the brain. Given the close relationship between NFT accumulation and neuronal loss, the mPRC reflects a promising structural marker for early diagnosis of AD. However, only limited tools that automatically measure the cortical thickness of the mPRC are currently available. Utilizing the nnU-Net framework, we trained models on structural MRI of 126 adults, with manually segmented labels as ground truth. These models were then applied to an independent dataset of 103 adults (comprising patients with Alzheimer’s dementia, amnestic mild cognitive impairment (aMCI), and healthy controls). High agreement was observed between manual and automated measurements of cortical thickness. Furthermore, we found significant atrophy in the Alzheimer’s dementia group in the mPRC, ERC, and lPRC compared to healthy controls. Comparison of the aMCI group and healthy controls revealed significant differences in the ERC only. The results underscore the utility of our automated segmentation tool in advancing Alzheimer’s research.