基于空洞空间卷积池化金字塔结构和注意力机制的全卷积残差网络磨玻璃肺结节分割方法

Segmentation of ground glass pulmonary nodules using full convolution residual network based on atrous spatial pyramid pooling structure and attention mechanism

准确分割磨玻璃肺结节(GGN)具有重要临床意义。针对电子计算机断层扫描(CT)图像中GGN边界模糊、形状不规则、强度不均匀等特点导致其分割困难的问题,本文提出一种全卷积残差网络算法,即基于空洞空间卷积池化金字塔结构和注意力机制的残差网络(ResAANet)算法。该网络算法利用空洞空间卷积池化金字塔(ASPP)结构扩大特征图感受野,提取更充分的目标特征,并采用注意力机制、残差连接和长跳跃连接充分保留卷积层提取的GGN敏感特征。首先,用上海市胸科医院收集的565个GGN对ResAANet进行全监督训练、验证,得到稳定的模型;然后,利用收集的另84个GGN和肺部图像数据库联盟(LIDC)公共数据库中145个GGN分别测试模型得到粗分割结果;最后,用连通域分析方法去除假阳性区域得到优化结果。本文所提算法在采集的临床数据和LIDC测试集上的戴斯相似系数(DSC)达到83.46%、83.26%,平均重合度(IoU)达到72.39%、71.56%,切片分割效率达到0.1 s/张。与其他算法相比,本文提出的方法能准确、快速分割GGN,且具有较好的稳健性,可以为医生提供结节大小、密度等重要信息,辅助医生后续的诊断和治疗。

Accurate segmentation of ground glass nodule(GGN)is important in clinical.But it is a tough work to segment the GGN,as the GGN in the computed tomography images show blur boundary,irregular shape,and uneven intensity.This paper aims to segment GGN by proposing a fully convolutional residual network,i.e.,residual network based on atrous spatial pyramid pooling structure and attention mechanism(ResAANet).The network uses atrous spatial pyramid pooling(ASPP)structure to expand the feature map receptive field and extract more sufficient features,and utilizes attention mechanism,residual connection,long skip connection to fully retain sensitive features,which is extracted by the convolutional layer.First,we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet,so as to obtain a stable model.Then,two groups of data selected from clinical examinations(84 GGN)and lung image database consortium(LIDC)dataset(145 GGN)were employed to validate and evaluate the performance of the proposed method.Finally,we apply the best threshold method to remove false positive regions and obtain optimized results.The average dice similarity coefficient(DSC)of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%,83.26%respectively,the average Jaccard index(IoU)reached 72.39%,71.56%respectively,and the speed of segmentation reached 0.1 seconds per image.Comparing with other reported methods,our new method could segment GGN accurately,quickly and robustly.It could provide doctors with important information such as nodule size or density,which assist doctors in subsequent diagnosis and treatment.