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nnUNet用于自动脏和囊细分在自体主导多性脏疾病

Chetana Krishnan¹, Emma Schmidt¹, Ezinwanne Onuoha¹

¹Departments of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA.

Current medical imaging

|February 23, 2024

概括

这项研究表明,nnUNet AI模型在MRI扫描中准确地细分和囊,用于患有自身主导多性病 (ADPKD) 的患者. 这种工具有助于监测疾病进展和治疗效果.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
脏疾病研究脏疾病研究

背景情况:

自体主导多囊性病 (ADPKD) 是一种遗传性疾病,其特征是囊的不受控制的生长.
这种囊生长导致体积增加,最终导致功能丧失.
脏总体积 (TKV) 和囊负担是ADPKD的预后成像生物标志物.

研究的目的:

评估nnUNet模型的性能,用于对脏和囊进行自动细分.
评估nnUNet在对ADPKD患者的T2加权 (T2W) MRI图像进行细分方面的实用性.

主要方法:

利用了来自CRISP队列的95名ADPKD患者的756张T2WMRI图像.
训练,验证和测试nnUNet模型,确保跨集的患者数据独家性,以防止偏差.
通过使用子相似系数 (DSC),交界线 (IoU) 和豪斯多夫距离 (HD) 评估细分精度.

主要成果:

达到高性能,测试DSC值为0.96±0.01对于脏和0.90±0.05对于囊.
报告的IOU得分为0.91±0.09对于脏和0.81±0.06对于囊.
证明脏的豪斯多夫距离为12.49±8.71毫米,囊的距离为12.04±10.41毫米.

结论:

nnUNet模型可靠地自动化T2WMRI扫描中的脏和囊体积量化.
这种自动化细分对于ADPKD预后有价值.
该工具支持在ADPKD患者中有效的治疗监测.

关键词:

在 ADPKDD 中,深度学习是一种深度学习.的图像细分的图像细分脏囊图像细分脏囊图像细分脏总体积脏总体积在线网址: nnUNet.

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