Abstract

Info

Title:: Early esophageal cancer image segmentation based on contextual feature awareness and dual frequency upsampling

Author(s):: MENG Yanzong1; LI Xiaoxia1; 2; ZHOU Yingyue1; 2; WEN Liming3; QIN Jiamin3; LIU Shuangli1; 2; 1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Mianyang 621000, China 3. Department of Gastroenterology, Sichuan Mianyang 404 Hospital, Mianyang 621000, China

Keywords:: Keywords: early esophageal cancer contextual feature awareness attention mechanism dilated convolution dual frequency upsampling

Abstract:: Abstract: Objective To propose a network for early esophageal cancer image segmentation using U-net with contextual feature awareness module and dual frequency upsampling module which solves the problem of loss of detailed information such as lesion edges during image segmentation. Methods The contextual feature awareness module improved with the attention mechanism and separable dilated convolution was used to obtain full-text contextual information and extract more feature details. The dual frequency upsampling module was adopted for upsampling from high frequency and low frequency, thereby effectively reducing the aliasing effect caused by pixel interpolation, minimizing the checkerboard effect caused by transposed convolution during single upsampling, and avoiding the loss of detail information. Results The mean intersection over union, sensitivity and specificity of the proposed method reached 80.34%, 87.47%, and 91.53%, respectively. Conclusion The proposed model is superior to mainstream semantic segmentation models such as nnU-Net for it can retain more detailed information and improve the accuracy of early esophageal cancer image segmentation.