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评估U-Net在短路段的细分:转移到临床MRI常规

Hohana Gabriela Konell¹, Luiz Otávio Murta Junior², Antônio Carlos Dos Santos³

¹Inbrain Lab, Department of Physics, Faculty of Philosophy, Sciences and Letters, University of São Paulo, Ribeirão Preto, SP, Brazil.

Magnetic resonance imaging

|May 16, 2024

概括

结合多样化的扩散加权成像数据集,可以显著改善U-Net.

科学领域:

神经成像是一种神经成像.
医学图像分析医学图像分析
计算神经科学是一种神经科学.

背景情况:

准确的结构连接映射对于理解大脑功能和神经疾病至关重要.
短白质道存在细分挑战,特别是临床质量的扩散权重成像 (DWI) 数据.
U-Net网络对管道细分有希望,但需要针对各种数据制定强有力的培训策略.

研究的目的:

评估U-Net网络在使用在各种实验条件下获取的DWI数据来细分短白质段的能力.
为了比较在不同数据集和培训策略上训练的U-Net模型的性能.

主要方法:

通过使用健康受试者的DWI数据进行了三项训练实验:专门使用人类连接组项目 (HCP) 数据,当地医院的临床数据,以及结合两者的混合方法.
在不同扫描系统上获得的患者和健康受试者的未见的DWI数据上测试了表现最佳的模型.
解析度标准化适用于数据集.

主要成果:

混合训练方法显著优于单个数据集上训练的模型,在当地医院数据集中为短路段提供了0.60-0.65的Dice分数.
这种混合模型显示出与仅在临床数据上训练的模型相比的实质性改善 (迪斯得分为0.37-0.50).
增强的性能普遍适用于各种扫描仪采购和患者群体,包括患者.

结论:

将多样化的DWI数据集与分辨率标准化相结合,可以增强U-Net对短路段分段的概括性.
短路段细分性能高度依赖于培训,验证和测试数据组成.
虽然有希望,但在将这种方法应用于来自不同实验条件的数据时,建议谨慎使用.

关键词:

深度学习是一种深度学习.短途旅行是短途旅行.曲谱学曲谱学曲谱学曲谱学白质细分的细分是白质的细分.

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