HRGUNet:新型高分辨率生成对抗网络与改进的 UNet 方法相结合用于脑肿瘤分割

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Visual Communication and Image Representation Pub Date : 2024-11-19 DOI:10.1016/j.jvcir.2024.104345
Dongmei Zhou, Hao Luo, Xingyang Li, Shengbing Chen
{"title":"HRGUNet:新型高分辨率生成对抗网络与改进的 UNet 方法相结合用于脑肿瘤分割","authors":"Dongmei Zhou,&nbsp;Hao Luo,&nbsp;Xingyang Li,&nbsp;Shengbing Chen","doi":"10.1016/j.jvcir.2024.104345","DOIUrl":null,"url":null,"abstract":"<div><div>Brain tumor segmentation in MRI images is challenging due to variability in tumor characteristics and low contrast. We propose HRGUNet, which combines a high-resolution generative adversarial network with an improved UNet architecture to enhance segmentation accuracy. Our proposed GAN model uses an innovative discriminator design that is able to process complete tumor labels as input. This approach can better ensure that the generator produces realistic tumor labels compared to some existing GAN models that only use local features. Additionally, we introduce a Multi-Scale Pyramid Fusion (MSPF) module to improve fine-grained feature extraction and a Refined Channel Attention (RCA) module to enhance focus on tumor regions. In comparative experiments, our method was verified on the BraTS2020 and BraTS2019 data sets, and the average Dice coefficient increased by 1.5% and 1.2% respectively, and the Hausdorff distance decreased by 23.9% and 15.2% respectively, showing its robustness and generalization for segmenting complex tumor structures.</div></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":"105 ","pages":"Article 104345"},"PeriodicalIF":2.6000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HRGUNet: A novel high-resolution generative adversarial network combined with an improved UNet method for brain tumor segmentation\",\"authors\":\"Dongmei Zhou,&nbsp;Hao Luo,&nbsp;Xingyang Li,&nbsp;Shengbing Chen\",\"doi\":\"10.1016/j.jvcir.2024.104345\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Brain tumor segmentation in MRI images is challenging due to variability in tumor characteristics and low contrast. We propose HRGUNet, which combines a high-resolution generative adversarial network with an improved UNet architecture to enhance segmentation accuracy. Our proposed GAN model uses an innovative discriminator design that is able to process complete tumor labels as input. This approach can better ensure that the generator produces realistic tumor labels compared to some existing GAN models that only use local features. Additionally, we introduce a Multi-Scale Pyramid Fusion (MSPF) module to improve fine-grained feature extraction and a Refined Channel Attention (RCA) module to enhance focus on tumor regions. In comparative experiments, our method was verified on the BraTS2020 and BraTS2019 data sets, and the average Dice coefficient increased by 1.5% and 1.2% respectively, and the Hausdorff distance decreased by 23.9% and 15.2% respectively, showing its robustness and generalization for segmenting complex tumor structures.</div></div>\",\"PeriodicalId\":54755,\"journal\":{\"name\":\"Journal of Visual Communication and Image Representation\",\"volume\":\"105 \",\"pages\":\"Article 104345\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Visual Communication and Image Representation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1047320324003018\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Communication and Image Representation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047320324003018","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

由于肿瘤特征的多变性和低对比度,磁共振成像图像中的脑肿瘤分割具有挑战性。我们提出的 HRGUNet 将高分辨率生成式对抗网络与改进的 UNet 架构相结合,以提高分割精度。我们提出的 GAN 模型采用了创新的判别器设计,能够将完整的肿瘤标签作为输入进行处理。与一些仅使用局部特征的现有 GAN 模型相比,这种方法能更好地确保生成器生成真实的肿瘤标签。此外,我们还引入了多尺度金字塔融合(MSPF)模块来改进细粒度特征提取,并引入了精细通道关注(RCA)模块来加强对肿瘤区域的关注。在对比实验中,我们的方法在 BraTS2020 和 BraTS2019 数据集上得到了验证,平均 Dice 系数分别增加了 1.5% 和 1.2%,Hausdorff 距离分别减少了 23.9% 和 15.2%,显示了该方法在分割复杂肿瘤结构时的鲁棒性和普适性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
HRGUNet: A novel high-resolution generative adversarial network combined with an improved UNet method for brain tumor segmentation
Brain tumor segmentation in MRI images is challenging due to variability in tumor characteristics and low contrast. We propose HRGUNet, which combines a high-resolution generative adversarial network with an improved UNet architecture to enhance segmentation accuracy. Our proposed GAN model uses an innovative discriminator design that is able to process complete tumor labels as input. This approach can better ensure that the generator produces realistic tumor labels compared to some existing GAN models that only use local features. Additionally, we introduce a Multi-Scale Pyramid Fusion (MSPF) module to improve fine-grained feature extraction and a Refined Channel Attention (RCA) module to enhance focus on tumor regions. In comparative experiments, our method was verified on the BraTS2020 and BraTS2019 data sets, and the average Dice coefficient increased by 1.5% and 1.2% respectively, and the Hausdorff distance decreased by 23.9% and 15.2% respectively, showing its robustness and generalization for segmenting complex tumor structures.
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
Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation 工程技术-计算机:软件工程
CiteScore
5.40
自引率
11.50%
发文量
188
审稿时长
9.9 months
期刊介绍: The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.
期刊最新文献
Global–local prompts guided image-text embedding, alignment and aggregation for multi-label zero-shot learning Contour-based object forecasting for autonomous driving FormerPose: An efficient multi-scale fusion Transformer network based on RGB-D for 6D pose estimation DetailCaptureYOLO: Accurately Detecting Small Targets in UAV Aerial Images Person re-identification transformer with patch attention and pruning
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1