【摘要】 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。更多还原

【Abstract】 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.更多还原