Abstract

Info

Title:: Lung nodule segmentation algorithm integrating Vnet and boundary features

Author(s):: JIANG Yueying1; SHI Yiping1; WENG Xiaojun2; ZHU Yamei1; DENG Yuan1; LIU Jin1; 1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China 2. Gao Broad Healthcare Group Shanghai Artemed Hospital, Shanghai 200003, China

Keywords:: Keywords: lung nodule segmentation Vnet network dilated convolution attention mechanism boundary key point selection algorithm

Abstract:: Abstract: Objective To improve BSR-Vnet algorithm based on Vnet for solving the problems of boundary information loss and boundary segmentation blurring in the process of lung nodule segmentation. Methods The boundary feature enhancement block was constructed using boundary key point selection algorithm and dilated convolution, and then it was integrated into the encoder to retain more boundary information. Then, the double attention mechanism was introduced by spatial and channel squeeze and excitation block to replace the bottleneck structure of Vnet for extracting non-local contextual information. Finally, the original residual structure of Vnet was used to construct the residual dilated block instead of the original decoder by the hybrid null convolution, thereby expanding the perceptual field and extracting more feature details. Results The improved BSR-Vnet realized a Dice coefficient of 87.94% on the public data set LDC-IDRI. Conclusion The proposed model effectively retains more boundary structure and information, and extracts more context information, which makes the lung nodule segmentation more accurate.