Li Jiao, Ye Jinyu, Zhang Ruixin, Wu Yue, Berhane Gebremedhin Samuel, Deng Hongxia, Shi Hong
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.
School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen, China.
Front Neurosci. 2023 Dec 7;17:1288366. doi: 10.3389/fnins.2023.1288366. eCollection 2023.
The application of U-shaped convolutional neural network (CNN) methods in medical image segmentation tasks has yielded impressive results. However, this structure's single-level context information extraction capability can lead to problems such as boundary blurring, so it needs to be improved. Additionally, the convolution operation's inherent locality restricts its ability to capture global and long-distance semantic information interactions effectively. Conversely, the transformer model excels at capturing global information.
Given these considerations, this paper presents a transformer fusion context pyramid medical image segmentation network (CPFTransformer). The CPFTransformer utilizes the Swin Transformer to integrate edge perception for segmentation edges. To effectively fuse global and multi-scale context information, we introduce an Edge-Aware module based on a context pyramid, which specifically emphasizes local features like edges and corners. Our approach employs a layered Swin Transformer with a shifted window mechanism as an encoder to extract contextual features. A decoder based on a symmetric Swin Transformer is employed for upsampling operations, thereby restoring the resolution of feature maps. The encoder and decoder are connected by an Edge-Aware module for the extraction of local features such as edges and corners.
Experimental evaluations on the Synapse multi-organ segmentation task and the ACDC dataset demonstrate the effectiveness of our method, yielding a segmentation accuracy of 79.87% (DSC) and 20.83% (HD) in the Synapse multi-organ segmentation task.
The method proposed in this paper, which combines the context pyramid mechanism and Transformer, enables fast and accurate automatic segmentation of medical images, thereby significantly enhancing the precision and reliability of medical diagnosis. Furthermore, the approach presented in this study can potentially be extended to image segmentation of other organs in the future.
U型卷积神经网络（CNN）方法在医学图像分割任务中的应用取得了令人瞩目的成果。然而，这种结构的单级上下文信息提取能力可能会导致边界模糊等问题，因此需要改进。此外，卷积操作固有的局部性限制了其有效捕捉全局和长距离语义信息交互的能力。相反，Transformer模型在捕捉全局信息方面表现出色。
考虑到这些因素，本文提出了一种Transformer融合上下文金字塔医学图像分割网络（CPFTransformer）。CPFTransformer利用Swin Transformer来整合分割边缘的边缘感知。为了有效地融合全局和多尺度上下文信息，我们引入了一种基于上下文金字塔的边缘感知模块，该模块特别强调边缘和角落等局部特征。我们的方法采用具有移位窗口机制的分层Swin Transformer作为编码器来提取上下文特征。基于对称Swin Transformer的解码器用于上采样操作，从而恢复特征图的分辨率。编码器和解码器通过边缘感知模块连接，用于提取边缘和角落等局部特征。
在Synapse多器官分割任务和ACDC数据集上的实验评估证明了我们方法的有效性，在Synapse多器官分割任务中分割准确率达到79.87%（DSC）和20.83%（HD）。
本文提出的方法结合了上下文金字塔机制和Transformer，能够实现医学图像的快速准确自动分割，从而显著提高医学诊断的精度和可靠性。此外，本研究提出的方法未来有可能扩展到其他器官的图像分割。
