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基于条件生成对抗网络的低级别胶质瘤MR图像分割

Low-Grade Gliomas MR Image Segmentation Based on Conditional Generative Adversarial Networks

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摘要

针对深度学习算法在脑肿瘤分割中存在标记数据不足的问题,提出了一种基于条件生成对抗网络(CGAN)的低级别胶质瘤(LGG)磁共振(MR)图像自动分割方法。首先,使用原始数据集训练CGAN并生成LGG图像以扩充原始数据集;然后,利用生成图像预训练分割网络;最后,在预训练模型的基础上训练分割模型。实验结果表明,相比常规数据扩充方法,本方法的Dice系数提高了4.39%,Jaccard指数提高了4.42%,为基于MR图像的LGG分割计算机铺助诊断系统提供了参考。

Abstract

In order to solve the problem that deep learning algorithm has insufficient labeled data in brain tumor segmentation, in this paper, an automatic segmentation method of low-grade gliomas (LGG) magnetic resonance (MR) images based on conditional generative adversarial networks (CGAN) is proposed. First, the original dataset is used to train the CGAN and generate LGG images to expand the original dataset. Then, the generated images are used to pre-train a segment network. Finally, the segmentation model is trained on the basis of the pre-training model. Experimental results show that compared with traditional data augmentation methods, the proposed method improves the Dice coefficient by 4.39% and Jaccard index by 4.42%. The method provides a reference for the development of a computer assisted diagnosis system for LGG segmentation based on MR images.

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中图分类号:TP391

DOI:10.3788/LOP57.221004

所属栏目:图像处理

基金项目:国家自然科学基金、陕西省科技厅自然科学基础研究计划;

收稿日期:2020-01-15

修改稿日期:2020-04-01

网络出版日期:2020-11-01

作者单位    点击查看

艾玲梅:陕西师范大学计算机科学学院, 陕西 西安 710119
石康珍:陕西师范大学计算机科学学院, 陕西 西安 710119

联系人作者:艾玲梅(almsac@163.com); 石康珍(almsac@163.com);

备注:国家自然科学基金、陕西省科技厅自然科学基础研究计划;

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引用该论文

Ai Lingmei,Shi Kangzhen. Low-Grade Gliomas MR Image Segmentation Based on Conditional Generative Adversarial Networks[J]. Laser & Optoelectronics Progress, 2020, 57(22): 221004

艾玲梅,石康珍. 基于条件生成对抗网络的低级别胶质瘤MR图像分割[J]. 激光与光电子学进展, 2020, 57(22): 221004

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