As one of the non-invasive imaging techniques, myocardial perfusion imaging provides a basis for the diagnosis of myocardial ischemia in coronary heart disease. Aiming at the bull-eye image in myocardial perfusion imaging, this paper proposed a branching structure, which included multi-layer transposed convolution up-sampling concatenate module and four-channel weighted channels attention module, and the output results of the branch structure were fused with the output results of trunk U-Net, to achieve accurate segmentation of the cardiac ischemia missing degree in myocardial perfusion bull-eye image. The experimental results show that the multi-layer transposed convolution up-sampling concatenate module realizes the fusion of different depth feature maps, and effectively reduces the interference of the severe sparse degree which is similar to the missing degree on the segmentation. Four-channel weighted attention module can further improve the ability to distinguish between the two similar degrees and the ability to learn edge details of the targets, and retain more abundant edge details features. The experimental data came from Tianjin Medical University General Hospital, Tianjin TEDA Hospital, Tianjin First Central Hospital and Third Central Hospital. The Jaccard scores in the self-built dataset was 5.00% higher than that of U-Net. The model presented in this paper is superior to other optimized models based on U-Net, and the subjective evaluation meets the accuracy requirements for clinical diagnosis.