计算机应用研究2024，Vol.41Issue(7)：2195-2202,8.DOI:10.19734/j.issn.1001-3695.2023.08.0509
EnGAN:医学图像分割中的增强生成对抗网络
EnGAN:enhancement generative adversarial network in medical image segmentation
摘要
Abstract
The quality issues commonly found in original medical images,such as insufficient contrast,blurred details,and noise interference,make it difficult for existing medical image segmentation techniques to achieve new breakthroughs.This study focused on the enhancement of medical image data.Without significantly altering the appearance of the image,it im-proved the quality problems of the original image by adding specific pixel compensation and making subtle image adjustments,thereby enhancing the accuracy of image segmentation.Firstly,it introduced a new optimizer module,which generated a con-tinuous distribution space as the target domain for transfer.This optimizer module took the labels of the dataset as input and mapped the discrete label data to the continuous distribution of medical images.Secondly,it proposed an EnGAN model based on generative adversarial networks(GAN),and used the transfer target domain generated by the optimizer module to guide the target generation of the adversarial network,thereby implanting the knowledge of improving medical image quality into the model to achieve image enhancement.Based on the COVID-19 dataset,convolutional neural networks,including U-Net,U-Net+ResNet34,U-Net+Attn Res U-Net,were utilized as the backbone network in the experiment,and the Dice coeffi-cient and intersection over union reached 73.5％and 69.3％,75.1％and 70.5％,and 75.2％and 70.3％respectively.The empirical results demonstrate that the proposed medical image quality enhancement technology effectively improves the ac-curacy of segmentation while retaining the original features to the greatest extent,providing a more robust and efficient solution for subsequent medical image processing research.关键词
医学图像分割/图像质量/图像增强/域迁移/对抗生成网络Key words
medical image segmentation/image quality/image enhancement/domain migration/generative adversarial networks分类
信息技术与安全科学引用本文复制引用
邓尔强,秦臻,朱国淞..EnGAN:医学图像分割中的增强生成对抗网络[J].计算机应用研究,2024,41(7):2195-2202,8.基金项目
国家自然科学基金资助项目(62372083,62072074,62076054,62027827,62002047) （62372083,62072074,62076054,62027827,62002047）
四川省科技支持计划资助项目(2024NSFTD0005,2022JDJQ0039) （2024NSFTD0005,2022JDJQ0039）
电子科技大学医工结合基金资助项目(ZYGX2021YGLH212,ZYGX2022YGRH012) （ZYGX2021YGLH212,ZYGX2022YGRH012）
