Pulmonary Nodule Segmentation Network Based On RkcU-Net

Yi Luo, Miao Cao, Xu Chang
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Abstract

U-Net network is widely used in the field of medical image segmentation. The automatic segmentation and detection of lung nodules can help in the early detection of lung cancer. Therefore, in this paper, to solve the problems of small proportion of nodules in CT images, complex features and insufficient segmentation accuracy, an improved U-Net network based on residual network and attention mechanism was proposed. The feature extraction part of RkcU-Net network is based on Res2net, a variant of Resnet, and on which a feature extraction module with automatic selection of convolution kernel size is designed to perform multi-scale convolution inside the feature layer to form perceptual fields of different sizes. This module selects the appropriate convolution kernel size to extract lung nodule features in the face of different fine-grained lung nodules. Secondly, the Contextual Supplementary (CS) Block is designed to use the information of adjacent upper and lower layers to correct for the upper layer features, eliminating the discrepancy in the fusion of features at different levels. In this paper, the LUNA16 dataset was selected as the basis for lung nodule segmentation experiments. The method used in this dataset can achieve a iou of 80.59% and a DSC score of 89.25%. The network effectively improves the accuracy of lung nodule segmentation compared with other models. The results show that the method enhances the feature extraction ability of the network and improves the segmentation effect. In addition, the contribution of jump connections to information recovery should be noted.
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基于 RkcU-Net 的肺结节分割网络
U-Net 网络被广泛应用于医学图像分割领域。肺结节的自动分割和检测有助于肺癌的早期发现。因此,本文针对 CT 图像中结节比例小、特征复杂、分割精度不高等问题,提出了一种基于残差网络和注意力机制的改进型 U-Net 网络。RkcU-Net 网络的特征提取部分基于 Resnet 的变种 Res2net,在此基础上设计了一个自动选择卷积核大小的特征提取模块,在特征层内进行多尺度卷积,形成不同大小的感知场。面对不同细粒度的肺结节,该模块会选择合适的卷积核大小来提取肺结节特征。其次,设计了上下文补充(Contextual Supplementary,CS)块,利用相邻上下两层的信息对上层特征进行校正,消除了不同层次特征融合的差异。本文选择 LUNA16 数据集作为肺结节分割实验的基础。该方法在该数据集中的 iou 得分为 80.59%,DSC 得分为 89.25%。与其他模型相比,该网络有效地提高了肺结节分割的准确性。结果表明,该方法增强了网络的特征提取能力,提高了分割效果。此外,还应注意跳跃连接对信息恢复的贡献。
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