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基于深度学习的自动和囊细分自体主导多囊性病使用单个与多机构数据

Emma K Schmidt¹, Chetana Krishnan¹, Ezinwanne Onuoha¹

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

Clinical imaging

|December 15, 2023

概括

一个深度学习模型用于对自身主导多囊性病 (ADPKD) 的脏和囊区域进行细分,MRI图像显示了可比的准确性,无论是在单个或多机构数据上训练的. 较大的数据集提高了囊细分的准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
腎臟病學 (nephrology) 是一種醫學專業.

背景情况:

自体主导多囊性病 (ADPKD) 是一种遗传性疾病.
在MRI图像中对脏和囊体积的准确细分对于监测ADPKD进展至关重要.
深度学习模型为自动图像细分提供了潜力.

研究的目的:

用单一机构数据训练的深度学习模型与多机构数据的准确性进行比较,用于在ADPKDMRI图像中对脏和囊区域进行细分.
评估数据集大小和多样性对模型性能的影响.

主要方法:

在四个机构的756个ADPKDMRI扫描中的2D切片上使用TensorFlow训练了一个U-Net深度学习模型.
模型使用多机构数据组合和单个单一机构数据集进行训练.
在单独的验证和测试组中,使用子相似系数 (DSC) 评估性能.

主要成果:

在单个和多机构培训中,脏细分获得了高精度 (DSC 0.90-0.95),显示出最小的差异 (1-2%).
囊细分在多机构培训 (DSC 0.83-0.89) 与单机构培训 (DSC 0.66-0.86) 相比显示出更大的改善,差异高达20%.
模型性能,特别是囊细分,随着更大的训练数据集而增加.

结论:

在单一机构的ADPKDMRI数据上训练的深度学习模型可以达到与在多机构数据上训练的细分模型相比较的准确性.
多机构培训显著提高了ADPKD中囊细分的准确性,突出了更大,更多样化的数据集的好处.
这些发现表明,虽然单一机构模型是可行的,但更大的数据集对于像ADPKD囊这样的复杂结构产生了更好的结果.

关键词:

自体主导多囊性病 (ADPKD) 是一种自体主导多囊性病.深度学习 (DL) 是指深度学习.的图像细分的图像细分脏囊图像细分脏囊图像细分联合国网络联合国网络联合国网络

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