Abstract

Info

Title:: Detection and segmentation of pulmonary nodules using improved 3DV-Net

Author(s):: LIU Fang1; SUN Peng2; CHEN Zhencheng3; 1. School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541000, China 2. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China 3. Key Laboratory of Guangxi Colleges and Universities for Biosensors and Instruments, School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541000, China

Keywords:: Keywords: pulmonary disease CT image pulmonary nodule segmentation 3DV-Net deep learning

Abstract:: Abstract: Objective To propose a deep learning-based algorithm for the recognition and segmentation of pulmonary nodules, thereby assisting doctors in the diagnosis of pulmonary diseases. Methods In view of the large amount of data in LUNA16 data set and the diversity of types and sizes of pulmonary nodules, an improved deep neural network 3DV-Net was adopted to complete the detection and segmentation of various pulmonary nodules, and then ResNet was used to classify the nodule and non-nodule images. The lung CT images in LUNA16 data set were preprocessed by image denoising and interpolation sampling. After coarse segmentation images and mask images were generated, the improved 3DV-Net model was used to carry out multiple training and prediction. The improved 3DV-Net network adopted skip block to solve the problem that with the deeper network level, the probability of gradient dissipation, gradient explosion and other issues was greater. Results The Dice similarity coefficient and IoU of the improved 3DV-Net reached 88.29% and 88.25%, respectively. Conclusion The proposed method is helpful to the detection and segmentation of pulmonary nodules and is of great significance in the auxiliary diagnosis of pulmonary nodules.