自动细分和放射学用于识别和评估克罗恩病中CTE病变的活动
Yankun Gao1, Bo Zhang1, Dehan Zhao2
1Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Inflammatory bowel diseases
|November 27, 2023
概括
一个深度学习模型准确地细分克罗恩氏病.
科学领域:
- 医学成像分析 医学成像分析
- 放射学中的人工智能
- 计算病理学计算病理学
背景情况:
- 克罗恩病 (CD) 诊断和活动评估依赖于计算机断层扫描 (CTE) 肠道学.
- 在CTE中CD病变的手动细分是耗时和主观的.
- 对CD病变的放射学分析可以为疾病特征提供定量特征.
研究的目的:
- 开发和验证深度学习模型,用于在CTE图像中自动细分CD病变.
- 从细分的病变中提取放射性特征.
- 使用这些功能构建和评估机器学习分类器,以区分CD活动.
主要方法:
- 两个CTE数据集的回顾性分析 (n=84用于细分,n=193用于分类).
- 使用nnU-Net深度学习框架开发自动细分模型.
- 提取放射学特征并选择最佳特征,用于构建五个机器学习分类器.
主要成果:
- 深度学习细分模型在测试组中实现了0.824的Dice相似系数.
- 后勤回归分类器在区分CD活动方面表现最好.
- 后勤回归模型实现了AUC为0.862,灵敏度为0.697,特异性为0.840,准确度为0.759.
结论:
- 一个自动化的深度学习模型提供了CTE中克罗恩病病变的准确细分.
- 使用放射学特征的机器学习分类器有效区分CD活动.
- 这种综合方法可以帮助放射科医生快速准确地评估CD活动.
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