基于深度监督的生成对抗网络,用于解剖和功能图像融合

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Shiqiang Liu , Weisheng Li , Guofen Wang , Yuping Huang , Yin Zhang , Dan He
{"title":"基于深度监督的生成对抗网络,用于解剖和功能图像融合","authors":"Shiqiang Liu ,&nbsp;Weisheng Li ,&nbsp;Guofen Wang ,&nbsp;Yuping Huang ,&nbsp;Yin Zhang ,&nbsp;Dan He","doi":"10.1016/j.bspc.2024.107011","DOIUrl":null,"url":null,"abstract":"<div><div>Medical image fusion techniques improve single-image representations by integrating salient information from medical images of different modalities. However, existing fusion methods suffer from limitations, such as vanishing gradients, blurred details, and low efficiency. To alleviate these problems, a generative adversarial network based on deep supervision (DSGAN) is proposed. First, a two-branch structure is proposed to separately extract salient information, such as texture and metabolic information, from different modal images. Self-supervised learning is performed by building a new deep supervision module to enhance effective feature extraction. The fusion and multimodal input images are then placed in the discriminator for computation. Adversarial loss based on the Earth Mover’s distance ensures that more spatial frequency, gradient, and contrast information are maintained in a fusion image, and makes model training more stable. In addition, DSGAN is an end-to-end model that does not manually set up complex fusion rules. Compared with classic fusion methods, the proposed DSGAN retains rich texture details and edge information in the input image, fuses images faster, and exhibits superior performance in objective evaluation metrics.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":"100 ","pages":"Article 107011"},"PeriodicalIF":4.9000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A generative adversarial network based on deep supervision for anatomical and functional image fusion\",\"authors\":\"Shiqiang Liu ,&nbsp;Weisheng Li ,&nbsp;Guofen Wang ,&nbsp;Yuping Huang ,&nbsp;Yin Zhang ,&nbsp;Dan He\",\"doi\":\"10.1016/j.bspc.2024.107011\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Medical image fusion techniques improve single-image representations by integrating salient information from medical images of different modalities. However, existing fusion methods suffer from limitations, such as vanishing gradients, blurred details, and low efficiency. To alleviate these problems, a generative adversarial network based on deep supervision (DSGAN) is proposed. First, a two-branch structure is proposed to separately extract salient information, such as texture and metabolic information, from different modal images. Self-supervised learning is performed by building a new deep supervision module to enhance effective feature extraction. The fusion and multimodal input images are then placed in the discriminator for computation. Adversarial loss based on the Earth Mover’s distance ensures that more spatial frequency, gradient, and contrast information are maintained in a fusion image, and makes model training more stable. In addition, DSGAN is an end-to-end model that does not manually set up complex fusion rules. Compared with classic fusion methods, the proposed DSGAN retains rich texture details and edge information in the input image, fuses images faster, and exhibits superior performance in objective evaluation metrics.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":\"100 \",\"pages\":\"Article 107011\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809424010693\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424010693","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

医学影像融合技术通过整合不同模式医学影像中的突出信息来改进单图像表征。然而,现有的融合方法存在梯度消失、细节模糊和效率低等局限性。为了缓解这些问题,我们提出了一种基于深度监督的生成对抗网络(DSGAN)。首先,提出了一种双分支结构,分别从不同模态图像中提取纹理和代谢信息等显著信息。通过建立一个新的深度监督模块来进行自监督学习,从而提高特征提取的有效性。然后将融合图像和多模态输入图像放入判别器中进行计算。基于地球移动距离的对抗损失可确保在融合图像中保留更多的空间频率、梯度和对比度信息,并使模型训练更加稳定。此外,DSGAN 是一种端到端模型,无需手动设置复杂的融合规则。与传统的融合方法相比,所提出的 DSGAN 能保留输入图像中丰富的纹理细节和边缘信息,融合图像的速度更快,在客观评价指标上表现出更优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A generative adversarial network based on deep supervision for anatomical and functional image fusion
Medical image fusion techniques improve single-image representations by integrating salient information from medical images of different modalities. However, existing fusion methods suffer from limitations, such as vanishing gradients, blurred details, and low efficiency. To alleviate these problems, a generative adversarial network based on deep supervision (DSGAN) is proposed. First, a two-branch structure is proposed to separately extract salient information, such as texture and metabolic information, from different modal images. Self-supervised learning is performed by building a new deep supervision module to enhance effective feature extraction. The fusion and multimodal input images are then placed in the discriminator for computation. Adversarial loss based on the Earth Mover’s distance ensures that more spatial frequency, gradient, and contrast information are maintained in a fusion image, and makes model training more stable. In addition, DSGAN is an end-to-end model that does not manually set up complex fusion rules. Compared with classic fusion methods, the proposed DSGAN retains rich texture details and edge information in the input image, fuses images faster, and exhibits superior performance in objective evaluation metrics.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信