Abstract

Info

Title:: Medical image segmentation using improved Unet combined with dynamic threshold changed FCMSPCNN

Author(s):: DI Jing; MA Shuai; WANG Guodong; LIAN Jing; School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Keywords:: Keywords: CoA Unet pulse coupled neural network attention mechanism liver segmentation retinal vascular segmentation

Abstract:: Abstract: Aiming at the long training time and low precision of medical image segmentation model based on deep learning, a segmentation method combining the multi-scale context encoding and decoding structure of dynamic threshold changed FCMSPCNN with context attention Unet (CoA Unet) is proposed. Dynamic threshold changed FCMSPCNN is used to pre-segment the target rectangle and mask the background. The deeper convolution block with shortcut connection integrates features at different levels, and highlights the learning of target features through the attention gate. Furthermore, an improved multi-scale context extractor is added to the bottom layer of the codec to better extract target feature information. The model is verified on LiTs and DRIVE datasets, respectively. The Miou, Aver_HD and Aver_Dice of the proposed method are 0.890 5, 6.369 9, 0.947 7 for liver segmentation, and 0.589 2, 9.255 9, 0.740 9 for retinal vascular segmentation. The experiment reveals that the preprocessing can shorten the training time by 4.30%-20.33% and improve the segmentation accuracy by 2%-6%. Compared with other 5 segmentation methods, CoA Unet can achieve better overall segmentation performance.