Wu Zezhi, Li Xiaoshu, Zuo Jianhui
Department of Computer Science, Anhui Medical University, Hefei, Anhui, China.
Department of Radiology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Front Oncol. 2023 Mar 23;13:1084096. doi: 10.3389/fonc.2023.1084096. eCollection 2023.
Due to the small proportion of target pixels in computed tomography (CT) images and the high similarity with the environment, convolutional neural network-based semantic segmentation models are difficult to develop by using deep learning. Extracting feature information often leads to under- or oversegmentation of lesions in CT images. In this paper, an improved convolutional neural network segmentation model known as RAD-UNet, which is based on the U-Net encoder-decoder architecture, is proposed and applied to lung nodular segmentation in CT images.
The proposed RAD-UNet segmentation model includes several improved components: the U-Net encoder is replaced by a ResNet residual network module; an atrous spatial pyramid pooling module is added after the U-Net encoder; and the U-Net decoder is improved by introducing a cross-fusion feature module with channel and spatial attention.
The segmentation model was applied to the LIDC dataset and a CT dataset collected by the Affiliated Hospital of Anhui Medical University. The experimental results show that compared with the existing SegNet [14] and U-Net [15] methods, the proposed model demonstrates better lung lesion segmentation performance. On the above two datasets, the mIoU reached 87.76% and 88.13%, and the F1-score reached 93.56% and 93.72%, respectively. Conclusion: The experimental results show that the improved RAD-UNet segmentation method achieves more accurate pixel-level segmentation in CT images of lung tumours and identifies lung nodules better than the SegNet [14] and U-Net [15] models. The problems of under- and oversegmentation that occur during segmentation are solved, effectively improving the image segmentation performance.
由于计算机断层扫描（CT）图像中目标像素比例小且与周围环境相似度高，基于卷积神经网络的语义分割模型难以通过深度学习进行开发。提取特征信息时，CT图像中的病变常出现分割不足或过度分割的情况。本文提出了一种基于U-Net编码器-解码器架构的改进卷积神经网络分割模型RAD-UNet，并将其应用于CT图像中的肺结节分割。
所提出的RAD-UNet分割模型包含几个改进组件：用ResNet残差网络模块替换U-Net编码器；在U-Net编码器之后添加空洞空间金字塔池化模块；通过引入具有通道和空间注意力的交叉融合特征模块改进U-Net解码器。
将该分割模型应用于LIDC数据集和安徽医科大学附属第一医院收集的CT数据集。实验结果表明，与现有的SegNet[14]和U-Net[15]方法相比，所提模型在肺病变分割性能上表现更优。在上述两个数据集上，平均交并比分别达到87.76%和88.13%，F1分数分别达到93.56%和93.72%。结论：实验结果表明，改进后的RAD-UNet分割方法在肺肿瘤CT图像中实现了更精确的像素级分割，在识别肺结节方面优于SegNet[14]和U-Net[15]模型。解决了分割过程中出现的分割不足和过度分割问题，有效提高了图像分割性能。
