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在CT扫描中基于深度学习的脑膜和心室系统的细分在存在异常的情况下

Annika Gerken¹, Sina Walluscheck², Peter Kohlmann³

¹Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.

Frontiers in neuroimaging

|August 9, 2023

概括

将脑出血数据添加到深度学习培训中,可以显著改善CT扫描中脑结构的细分. 这种增强的稳定性有利于对正常和病态脑部扫描的分析,有助于临床诊断.

科学领域:

医疗成像医学成像
人工智能的人工智能
神经科学是一个神经科学.

背景情况:

准确细分大脑外围膜和脑脊液空间对于分析大脑CT数据至关重要.
对于临床诊断应用而言,对解剖学变异和病理的强度至关重要.

研究的目的:

调查结合出血训练数据对深度学习细分算法的稳定性的影响.
评估算法在正常,出血和瘤大脑CT数据集上的性能.

主要方法:

在正常的大脑解剖学数据上训练了一个2D U-Net深度学习模型.
第二个模型使用相同的数据和额外的脑出血受试者进行了训练.
这两种模型都在单独的正常,出血和瘤 (GLIS-RT数据集) 测试案例上进行了评估.

主要成果:

与仅在正常数据上训练的模型相比,使用出血数据的训练显著改善了所有测试集的细分性能.
改进的算法获得了高的子中位数得分:0.98 (大脑膜),0.91 (左心室),0.90 (右心室) 在出血测试数据上.
在瘤测试组中,Dice的中位数得分为0.96 (腹膜),0.90 (左心室),0.90 (右心室),证明了更好的概括性.

结论:

将病理学数据扩展到训练数据集对于提高脑CT细分算法的稳定性至关重要.
改进的算法证明了对细分大脑副体和心室系统的稳定性增加,即使对于看不见的异常.
进一步扩展训练数据,包括各种病理,可以提高算法的概括性.

关键词:

深度学习是一种深度学习.发生出血发生出血围体内膜 (parenchyma) 是一个体内膜.细分化细分化的细分化腹腔系统腹腔系统腹腔系统

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