HiFi-Syn: Hierarchical granularity discrimination for high-fidelity synthesis of MR images with structure preservation

IF 10.7 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Medical image analysis Pub Date : 2024-11-19 DOI:10.1016/j.media.2024.103390
Ziqi Yu , Botao Zhao , Shengjie Zhang , Xiang Chen , Fuhua Yan , Jianfeng Feng , Tingying Peng , Xiao-Yong Zhang
{"title":"HiFi-Syn: Hierarchical granularity discrimination for high-fidelity synthesis of MR images with structure preservation","authors":"Ziqi Yu ,&nbsp;Botao Zhao ,&nbsp;Shengjie Zhang ,&nbsp;Xiang Chen ,&nbsp;Fuhua Yan ,&nbsp;Jianfeng Feng ,&nbsp;Tingying Peng ,&nbsp;Xiao-Yong Zhang","doi":"10.1016/j.media.2024.103390","DOIUrl":null,"url":null,"abstract":"<div><div>Synthesizing medical images while preserving their structural information is crucial in medical research. In such scenarios, the preservation of anatomical content becomes especially important. Although recent advances have been made by incorporating instance-level information to guide translation, these methods overlook the spatial coherence of structural-level representation and the anatomical invariance of content during translation. To address these issues, we introduce hierarchical granularity discrimination, which exploits various levels of semantic information present in medical images. Our strategy utilizes three levels of discrimination granularity: pixel-level discrimination using a Brain Memory Bank, structure-level discrimination on each brain structure with a re-weighting strategy to focus on hard samples, and global-level discrimination to ensure anatomical consistency during translation. The image translation performance of our strategy has been evaluated on three independent datasets (UK Biobank, IXI, and BraTS 2018), and it has outperformed state-of-the-art algorithms. Particularly, our model excels not only in synthesizing normal structures but also in handling abnormal (pathological) structures, such as brain tumors, despite the variations in contrast observed across different imaging modalities due to their pathological characteristics. The diagnostic value of synthesized MR images containing brain tumors has been evaluated by radiologists. This indicates that our model may offer an alternative solution in scenarios where specific MR modalities of patients are unavailable. Extensive experiments further demonstrate the versatility of our method, providing unique insights into medical image translation.</div></div>","PeriodicalId":18328,"journal":{"name":"Medical image analysis","volume":"100 ","pages":"Article 103390"},"PeriodicalIF":10.7000,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical image analysis","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361841524003153","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Synthesizing medical images while preserving their structural information is crucial in medical research. In such scenarios, the preservation of anatomical content becomes especially important. Although recent advances have been made by incorporating instance-level information to guide translation, these methods overlook the spatial coherence of structural-level representation and the anatomical invariance of content during translation. To address these issues, we introduce hierarchical granularity discrimination, which exploits various levels of semantic information present in medical images. Our strategy utilizes three levels of discrimination granularity: pixel-level discrimination using a Brain Memory Bank, structure-level discrimination on each brain structure with a re-weighting strategy to focus on hard samples, and global-level discrimination to ensure anatomical consistency during translation. The image translation performance of our strategy has been evaluated on three independent datasets (UK Biobank, IXI, and BraTS 2018), and it has outperformed state-of-the-art algorithms. Particularly, our model excels not only in synthesizing normal structures but also in handling abnormal (pathological) structures, such as brain tumors, despite the variations in contrast observed across different imaging modalities due to their pathological characteristics. The diagnostic value of synthesized MR images containing brain tumors has been evaluated by radiologists. This indicates that our model may offer an alternative solution in scenarios where specific MR modalities of patients are unavailable. Extensive experiments further demonstrate the versatility of our method, providing unique insights into medical image translation.
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
HiFi-Syn:用于高保真合成磁共振图像并保留结构的分级粒度判别技术
在医学研究中,合成医学图像并保留其结构信息至关重要。在这种情况下,保留解剖学内容变得尤为重要。虽然最近在结合实例级信息指导翻译方面取得了进展,但这些方法忽略了结构级表示的空间一致性和翻译过程中内容的解剖不变性。为了解决这些问题,我们引入了分级粒度判别法,利用医学影像中不同层次的语义信息。我们的策略利用了三个层次的判别粒度:利用大脑记忆库进行像素级判别;对每个大脑结构进行结构级判别,并采用重新加权策略,以重点关注硬样本;以及进行全局级判别,以确保翻译过程中的解剖一致性。我们在三个独立数据集(英国生物库、IXI 和 BraTS 2018)上评估了我们策略的图像翻译性能,其表现优于最先进的算法。特别是,我们的模型不仅在合成正常结构方面表现出色,而且在处理异常(病理)结构(如脑肿瘤)方面也很出色,尽管不同成像模式因其病理特征而导致对比度不同。放射科医生已对包含脑肿瘤的合成 MR 图像的诊断价值进行了评估。这表明,在无法获得患者特定磁共振成像模式的情况下,我们的模型可以提供另一种解决方案。广泛的实验进一步证明了我们方法的多功能性,为医学图像翻译提供了独特的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
期刊最新文献
Corrigendum to "Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge" [Medical Image Analysis, April 2022, Volume 77, 102333]. Editorial for Special Issue on Foundation Models for Medical Image Analysis. Few-shot medical image segmentation with high-fidelity prototypes. The Developing Human Connectome Project: A fast deep learning-based pipeline for neonatal cortical surface reconstruction. AutoFOX: An automated cross-modal 3D fusion framework of coronary X-ray angiography and OCT.
×
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