基于多网络集成的脑白质高信号分割方法

White Matter Hyperintensities Segmentation Using Neural Network Ensembles

目的构建一种新型基于全卷积神经网络的分割方法,以提升脑白质高信号的分割精度。方法本模型基于U-Net的编码器-解码器结构,采用密集连接结构,并优化卷积次数,以充分利用网络中间层提取的特征,实现端到端的分割。该模型采用多个网络的集成框架,以提高模型的鲁棒性。模型对白质高信号的分割精度评价指标包括相似性系数、豪斯多夫距离、平均体积差异和F1分数。结果在公开数据集上进行的测试结果表明,本文提出的方法在4种分割评价标准(包括相似性系数,豪斯多夫距离,平均体积差异和F1分数)上的表现,优于现有的主流的分割方法,证明了该方法的有效性。结论基于密集连接和集成优化的神经网络模型,能对脑白质高信号进行较好的分割。该方法的提出,为进一步分析脑血管病白质特征,提供了重要的算法支撑。

Objective To construct a model applying the full convolutional neural network to improve the segmentation accuracy of white matter hyperintensities.Methods This model,which is based on the encoder-decoder architecture of U-Net,employs dense connections,and optimizes the number of convolutional layers to make full use of the features,and can implement end-to-end segmentation.Neural network ensembles are used to improve the robustness of the model.The performance metrics assessing this model included Dice similarity coefficient,Hausdorff distance,average volume difference,and F1-score.Results The experimental test results showed that this model achieved a high performance on various metrics(including Dice similarity coefficient,Hausdorff distance,average volume difference,and F1-score),outperforming the mainstream segmentation methods.The results proved the effectiveness of this method.Conclusions The models based on dense connections and ensemble can obtain a better model for white matter hyperintensities segmentation.This method provides an important algorithm reference for further analysis of white matter characteristics in cerebrovascular disease.