基于3D深度残差网络与级联U-Net的缺血性脑卒中病灶分割算法

Segmentation algorithm of ischemic stroke lesion based on 3D deep residual network and cascade U-Net

为了解决人工勾画缺血性脑卒中病灶费时费力且易引入主观差异的问题,提出了一种基于三维(3D)深度残差网络与级联U-Net的自动分割算法。首先,为了有效利用图像的3D上下文信息并改善类不平衡现象,将脑卒中核磁共振图像(MRI)采样成图像块作为网络输入;然后,利用基于3D深度残差网络与级联U-Net的分割模型对图像块进行特征提取,获得粗分割结果;最后,对粗分割结果进行精分割处理。在ISLES数据集上的实验结果表明,该算法的Dice系数可达到0.81,精确度可达到0.81,灵敏度可达到0.81,平均对称表面距离(ASSD)距离系数为1.32,HD为22.67。所提算法与3D U-Net算法、基于水平集算法、基于模糊C均值(FCM)算法和基于卷积神经网络(CNN)算法相比分割性能更好。

Artificial identification of ischemic stroke lesion is time-consuming,laborious and easy be added subjective differences.To solve this problem,an automatic segmentation algorithm based on 3D deep residual network and cascade U-Net was proposed.Firstly,in order to efficiently utilize 3D contextual information of the image and the solve class imbalance issue,the patches were extracted from the stroke Magnetic Resonance Image(MRI)and put into network.Then,a segmentation model based on 3D deep residual network and cascade U-Net was used to extract features of the image patches,and the coarse segmentation result was obtained.Finally,the fine segmentation process was used to optimize the coarse segmentation result.The experiment results show that,on the dataset of Ischemic Stroke LEsion Segmentation(ISLES),for the proposed algorithm,the Dice similarity coefficient reached 0.81,the recall reached 0.81 and the precision reached 0.81,the distance coefficient Average Symmetric Surface Distance(ASSD)reached 1.32 and Hausdorff Distance(HD)reached 22.67.Compared with 3D U-Net algorithm,level set algorithm,Fuzzy C-Means(FCM)algorithm and Convolutional Neural Network(CNN)algorithm,the proposed algorithm has better segmentation performance.