基于改进U-Net++的CT影像肺结节分割算法

卷积神经网络的语义分割模型未有效利用特征权重信息,导致在医学图像复杂场景中分割边界出现欠分割现象.针对该问题,基于融合自适应加权聚合策略提出一种改进的U-Net++网络,并将其应用于电子计算机断层扫描影像肺结节分割.该模型首先在卷积神经网络中提取出不同深度特征语义级别的信息,再结合权重聚合模块,自适应地学习各层特征的权...

Segmentation of Lung Nodules in CT Images Using Improved U-Net++

Convolutional neural network-based semantic segmentation models do not effectively explore feature weight information,which will result in under-segmentation of segmentation boundaries in complex scenes of computed tomography images.To address this problem,an improved U-Net++model is proposed to explore adaptive weighted aggregation strategy based on U-Net++,and the improved UNet++model is applied to the segmentation of lung nodules in computed tomography images.In the convolutional neural network phase,the information from the different levels of deep features is extracted and combined with the weighted aggregation module,and thus the weights of features in each layer are adaptively learned.Then the learned weights are loaded on each feature layer and obtained a sampled segmentation map,and the final segmentation result can be obtained.Segmentation experiments are carried out on the lung cancer data sets of LIDC and Chongqing University Cancer Hospital.The intersection over union of the proposed improved U-Net++method on two datasets reach 80.59%and 87.40%,and the DICE of this method on two datasets could reach 88.23%and 90.83%,respectively.Compared with UNet and U-Net++,the proposed algorithm significantly improves the segmentation performance of lung nodules in computed tomography images.The experimental results show that improved U-Net++achieves accurate segmentation on tiny details of tumors,and it bring beneifits to solve the problem of under segmentation when lung nodules grow invasively to the surrounding.