An FA-SegNet Image Segmentation Model Based on Fuzzy Attention and Its Application in Cardiac MRI Segmentation

Abstract

Aiming at the medical images segmentation with low-recognition and high background noise, a deep convolution neural network image segmentation model based on fuzzy attention mechanism is proposed, which is called FA-SegNet. It takes SegNet as the basic framework. In the down-sampling module for image feature extraction, a fuzzy channel-attention module is added to strengthen the discrimination of different target regions. In the up-sampling module for image size restoration and multi-scale feature fusion, a fuzzy spatial-attention module is added to reduce the loss of image details and expand the receptive field. In this paper, fuzzy cognition is introduced into the feature fusion of CNNs. Based on the attention mechanism, fuzzy membership is used to re-calibrate the importance of the pixel value in local regions. It can strengthen the distinguishing ability of image features, and the fusion ability of the contextual information, which improves the segmentation accuracy of the target regions. Taking MRI segmentation as an experimental example, multiple targets such as the left ventricles, right ventricles, and left ventricular myocardium are selected as the segmentation targets. The pixels accuracy is 92.47%, the mean intersection to union is 86.18%, and the Dice coefficient is 92.44%, which are improved compared with other methods. It verifies the accuracy and applicability of the proposed method for the medical images segmentation, especially the targets with low-recognition and serious occlusion.