Dan Yongping, Jin Weishou, Wang Zhida, Sun Changhao
School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou, Henan, China.
PeerJ Comput Sci. 2023 Aug 18;9:e1515. doi: 10.7717/peerj-cs.1515. eCollection 2023.
In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the "Chest Xray Masks and Labels" dataset, which is better than the full convolutional network or the combination of Transformer and convolution.
近年来，神经网络在医学成像领域取得了开创性成就。特别是，基于U形结构的深度神经网络被广泛应用于不同的医学图像分割任务中。为了改进肺部疾病的早期诊断和临床决策系统，利用神经网络进行肺部分割以辅助定位和观察形状已成为关键步骤。但仍存在精度较低的问题。为了实现更好的分割精度，本文提出了一种优化的纯Transformer U形分割方法。优化后的分割网络采用添加跳跃连接和进行特殊拼接处理的方法，减少了编码过程中的信息损失，增加了解码过程中的信息，从而达到提高分割精度的目的。最终实验表明，我们改进后的网络在“胸部X光掩码和标签”数据集的分割中准确率达到了97.86%，优于全卷积网络或Transformer与卷积的组合。
