Abstract: Echocardiogram segmentation plays a huge role in the diagnosis of physicians in clinical practice.To address the problems that ultrasound images contain a large amount of noise,contour features are not obvious,and existing segmentation algorithms are time-consuming,incomplete target segmentation or introduce unnecessary background regions,a real-time segmentation algorithm based on full convolutional network with geometric information-assisted segmentation is proposed.First,the improved YOLACT framework is used to generate the coefficients of the prototype template mask and the left and right ventricular and atrial instance masks in parallel,and they are combined linearly to obtain the instance mask.Then the encoder module is used to enhance the segmentation effect,the position encoding module is proposed to avoid the loss of global position information caused by the convolutional neural network,and the shape encoding module is proposed to reduce the classification error caused by the small difference between atria and ventricles.Then,the encoder module is used to enhance the segmentation effect.The experimental results show that that the APA,AMIoU and ADICE metrics on the echocardiographic image reach 0.777,0.705 and 0.827,respectively,and the method obtains results close to nnU-Net in terms of accuracy than other algorithms,but the speed can reach 27 frames per second,which is 145%better than UNet++.