在MRI中基于深度学习的肝囊片细分用于自身主导的多囊性病
Mina Chookhachizadeh Moghadam1,2, Mohit Aspal1, Xinzi He1,2,3
1Department of Radiology, Weill Cornell Medicine, New York, NY 10065, United States.
Radiology advances
|October 8, 2025
概括
深度学习模型现在可以自动检测和细分自身主导多囊性病 (ADPKD) 中的肝囊,改善多囊性肝病 (PLD) 的早期评估. 这种人工智能方法显著减少了注释时间,并增强了疾病监测.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 腎病學和肝病學.
背景情况:
- 自体主导性多囊性病 (ADPKD) 可以导致多囊性肝病 (PLD),其特征是肝囊.
- 虽然往往无症状,但PLD的扩大肝脏可能会导致需要干预的并发症.
- 目前测量肝体积的方法无法检测早期的PLD,当囊很小时.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于在PLD中自动检测和细分肝囊.
- 通过量化肝囊体体积来改善早期评估和追踪PLD.
主要方法:
- 基于UNet的深度学习平台 (nnU-Net) 使用40名ADPKD患者的注释性肝囊数据进行了训练.
- 该模型经过了内部,外部和测试-重新测试验证,使用诸如子得分和voxel级真实阳性率 (VTPR) 等指标.
- 评价了肝囊细分的节省时间和可重现性.
主要成果:
- DL模型实现了高性能,在内部和外部测试集上获得了80-82%的子得分.
- 它表现出很好的VTPR (75-91%),特别是在检测小的,难以手动注释的肝囊时.
- 标注时间平均减少了91%,测试重复测试的评估显示出高可重复性.
结论:
- 肝脏囊细分的深度学习自动化显示了在PLD中改善肝脏囊体积的跟踪的前景.
- 这种人工智能驱动的方法可以提高在ADPKD患者中多囊性肝病的早期检测和监测.
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