Medical Image Segmentation Model Based on Involution U-Net

Abstract

The main objects of image segmentation technology are natural images and medical images.Compared with that of natural images, semantic segmentation of medical images usually requires high accuracy for the subsequent steps of clinical analysis, diagnosis, and treatment planning.At present, the depth neural network model used to semantically segment medical images only considers the translation invariance of position, which features an insufficiently large local receptive field, leaving no way to express long-range dependence.Therefore, medical image segmentation model is proposed in this paper.Based on the Involution U-Net network, the involution operation replaces the traditional convolution operation, and the involution structure is adopted as the basic network structure to tailor the model's learning ability to the local features of medical images.To improve the model's learning ability, Besides, the attention mechanism module is introduced into the bottleneck layer to learn the long-range dependency of images.Experimental results on the lung CT dataset show that the model's Dice coefficient is 0.998, which is approximately 5% higher than that of the current segmentation model based on a convolutional neural network.In addition, this model greatly cuts the Hausdorff distance, apart from achieving higher segmentation accuracy and better robustness.