Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering,Tianjin University, Tianjin 300072, China.
School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China.
Comput Med Imaging Graph. 2022 Apr;97:102055. doi: 10.1016/j.compmedimag.2022.102055. Epub 2022 Mar 16.
Automatic vessel segmentation is a key step of clinical or pre-clinical vessel bio-markers for clinical diagnosis. In previous research, the segmentation architectures are mainly based on Convolutional Neural Networks (CNN). However, due to the limitation of the receipt of field (ROF) of convolution operation, it is difficult to further improve the accuracy of the CNN-based methods. To solve this problem, a Squeeze-Excitation Transformer U-net (SETUnet) is proposed to break the ROF limitation of CNN. The proposed squeeze-excitation Transformer can introduce the self attention mechanism into the vessel segmentation task by generating a global attention mapping according to the entire vessel image. To test the performance of the proposed SETUnet, the SETUnet is trained and tested on several public vessel data-sets. The results show that the SETUnet outperforms several state-of-the-art vessel segmentation neural networks, especially on the connectivity of the segmented vessels.
自动血管分割是临床或临床前血管生物标志物用于临床诊断的关键步骤。在以前的研究中，分割架构主要基于卷积神经网络（CNN）。然而，由于卷积运算的感受野（ROF）的限制，基于 CNN 的方法很难进一步提高精度。为了解决这个问题，提出了一种挤压激励变换 U 型网络（SETUnet）来打破 CNN 的 ROF 限制。所提出的挤压激励变换可以通过根据整个血管图像生成全局注意力映射，将自注意力机制引入血管分割任务中。为了测试所提出的 SETUnet 的性能，在几个公共血管数据集上对 SETUnet 进行了训练和测试。结果表明，SETUnet 优于几种先进的血管分割神经网络，特别是在分割血管的连通性方面。
