关键词: 超声心动图, 图像分割, octave卷积, 左心室

Abstract: Aiming at the problem that traditional Convolutional Neural Network（CNN）has low segmentation precision of left ventricle and feature redundancy in feature extraction process，based on traditional convolutional neural network，left ventricular echocardiography segmentation method based on octave convolution was proposed. Firstly，octave convolution was used to extract features from the images and divide the feature map into high frequency part and low frequency part， which reduces the use of low frequency information in the process of convolution，thus reducing the computational complexity of the network model. Secondly，a new loss function was proposed，which combined the cross entropy and the Dice coefficient. The experimental results show that the MIoU（Mean Intersection of Union）of the segmentation result can reach 79. 21% by using this method on the two-cavity dataset，which is 6. 1 percentage points higher than that of the traditional Unet convolutional neural network. The proposed method has high segmentation accuracy and low computational complexity.

Key words: echocardiography, image segmentation, octave convolution；left ventricle