在使用深度学习的圣关节MRI上对脂肪转化形成的自动细分
Xin Li1, Yi Lin2, Zhuoyao Xie1
1Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, 510630, Guangdong, China.
Insights into imaging
|March 26, 2024
概括
深度学习模型在神经关节MRI上准确地细分脂肪代谢,通过提高放射科医生的表现来帮助诊断和预测轴性脊柱关节炎 (axSpA).
科学领域:
- 医疗成像中的人工智能
- 放射学和诊断成像 放射学和诊断成像
- 风湿病学和临床免疫学
背景情况:
- 乳关节 (SIJ) MRI上的脂肪转化 (FM) 是一个关键的成像发现.
- 精确的FM细分对于诊断和监测轴性脊柱关节炎 (axSpA) 是至关重要的.
- 目前的手动细分方法可能耗时且主观.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于SIJMRI上的FM的自动细分.
- 建立一个DL模型,根据FM细分对axSpA与非axSpA进行分类.
- 评估DL模型辅助对射线科医生在FM细分中的表现的影响.
主要方法:
- 追溯收集了706个SIJMRI扫描,这些扫描来自患有FM的患者.
- 开发基于UNet的DL模型,用于FM细分.
- 创建一个使用细分输出的分类模型,以区分axSpA与非axSpA.
- 在内部和外部测试集上使用子相似系数 (DSC) 和曲线下的面积 (AUC) 进行评估.
主要成果:
- 细分模型实现了高精度,DSC为81.86% (内部) 和85.44% (外部).
- 该分类模型表现良好,AUC为0.876 (内部) 和0.799 (外部).
- 模型辅助显著改善了居民和专家放射科医生的细分性能.
结论:
- 深度学习为SIJMRI上的FM细分提供了一种新,准确和自动化的方法.
- 自动化细分促进了定量分析,潜在地改善了axSpA诊断和预后.
- DL模型可以提高放射科医生的表现,从而进行更有效,更准确的评估.
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