改进全卷积神经网络的甲状腺结节分割方法

Improved FCN Segmentation Method for Thyroid Nodules

为了更加精确地分割出甲状腺结节,本文提出了一种改进的全卷积神经网络(Fully convolutional network,FCN)分割模型。相较于FCN,本文方法加入了空洞空间卷积池化金字塔(Atrousspatialpyramidpooling,ASPP)模块与多层特征传递模块(Featuretransfer,FT),并采用LinkNet模型中Decoder模块进行上采样,VGG16主干网络实现特征提取下采样。实验采用来自斯坦福AIMI(Artificial intelligence in medicine and imaging)共享数据集的17413张超声甲状腺结节图像分别用于训练、验证和测试。实验结果表明,相比于其他多种分割模型,本文模型在平均交并比(mean Intersection over union,mIoU),Dice相似系数,F1分数3个分割指标上分别达到了79.7%,87.6%和98.42%,实现了更好的分割效果,有效地提升了甲状腺结节的分割精确度。

In order to segment thyroid nodules more accurately,this paper proposes an improved fully convolutional network(FCN)segmentation model.Compared with FCN,the atrous spatial pyramid pooling(ASPP)module and the multi-layer feature transfer(FT)module are added.The decoder module in LinkNet model is used for up-sampling,and the VGG16 backbone network is used for feature extraction down-sampling.The experiment uses 17413 ultrasound thyroid nodule images from Stanford AIMI shared data set for training,verification and testing,respectively.Experimental results show that compared with other segmentation models,the proposed model achieves 79.7%,87.6%and 98.42%in mean intersection over union(mIoU),Dice similarity coefficient and F1 score respectively,achieving better segmentation effect and effectively improving the segmentation accuracy of thyroid nodules.