血管扩散:基于扩散模型的三维血管结构生成

Zhanqiang Guo;Zimeng Tan;Jianjiang Feng;Jie Zhou
{"title":"血管扩散:基于扩散模型的三维血管结构生成","authors":"Zhanqiang Guo;Zimeng Tan;Jianjiang Feng;Jie Zhou","doi":"10.1109/TMI.2025.3568602","DOIUrl":null,"url":null,"abstract":"3D vascular structure models are pivotal in disease diagnosis, surgical planning, and medical education. The intricate nature of the vascular system presents significant challenges in generating accurate vascular structures. Constrained by the complex connectivity of the overall vascular structure, existing methods primarily focus on generating local or individual vessels. In this paper, we introduce a novel two-stage framework termed VesselDiffusion for the generation of detailed vascular structures, which is more valuable for medical analysis. Given that training data for specific vascular structure is often limited, direct generation of 3D data often results in inadequate detail and insufficient diversity. To this end, we initially train a 2D vascular generation model utilizing extensively available generic 2D vascular datasets. Taking the generated 2D images as input, a conditional diffusion model, integrating a dual-stream feature extraction (DSFE) module, is proposed to extrapolate 3D vascular systems. The DSFE module, comprising a Vision Transformer and a Graph Convolutional Network, synergistically captures visual features of global connection rationality and structural features of local vascular details, ensuring the authenticity and diversity of the generated 3D data. To the best of our knowledge, VesselDiffusion is the first model designed for generating comprehensive and realistic vascular networks with diffusion process. Comparative analyses with other generation methodologies demonstrate that the proposed framework achieves superior accuracy and diversity. Our code is available at: <monospace><uri>https://github.com/gzq17/VesselDiffusion</uri></monospace>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 9","pages":"3845-3857"},"PeriodicalIF":0.0000,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"VesselDiffusion: 3D Vascular Structure Generation Based on Diffusion Model\",\"authors\":\"Zhanqiang Guo;Zimeng Tan;Jianjiang Feng;Jie Zhou\",\"doi\":\"10.1109/TMI.2025.3568602\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"3D vascular structure models are pivotal in disease diagnosis, surgical planning, and medical education. The intricate nature of the vascular system presents significant challenges in generating accurate vascular structures. Constrained by the complex connectivity of the overall vascular structure, existing methods primarily focus on generating local or individual vessels. In this paper, we introduce a novel two-stage framework termed VesselDiffusion for the generation of detailed vascular structures, which is more valuable for medical analysis. Given that training data for specific vascular structure is often limited, direct generation of 3D data often results in inadequate detail and insufficient diversity. To this end, we initially train a 2D vascular generation model utilizing extensively available generic 2D vascular datasets. Taking the generated 2D images as input, a conditional diffusion model, integrating a dual-stream feature extraction (DSFE) module, is proposed to extrapolate 3D vascular systems. The DSFE module, comprising a Vision Transformer and a Graph Convolutional Network, synergistically captures visual features of global connection rationality and structural features of local vascular details, ensuring the authenticity and diversity of the generated 3D data. To the best of our knowledge, VesselDiffusion is the first model designed for generating comprehensive and realistic vascular networks with diffusion process. Comparative analyses with other generation methodologies demonstrate that the proposed framework achieves superior accuracy and diversity. Our code is available at: <monospace><uri>https://github.com/gzq17/VesselDiffusion</uri></monospace>.\",\"PeriodicalId\":94033,\"journal\":{\"name\":\"IEEE transactions on medical imaging\",\"volume\":\"44 9\",\"pages\":\"3845-3857\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on medical imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10994840/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on medical imaging","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10994840/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

三维血管结构模型在疾病诊断、手术计划和医学教育中至关重要。血管系统的复杂性质对生成精确的血管结构提出了重大挑战。受整体血管结构复杂连通性的限制,现有方法主要集中于生成局部或单个血管。在本文中,我们介绍了一种新的称为血管扩散的两阶段框架,用于生成详细的血管结构,这对医学分析更有价值。鉴于特定血管结构的训练数据往往有限,直接生成3D数据往往导致细节不足和多样性不足。为此,我们首先利用广泛可用的通用2D血管数据集训练2D血管生成模型。以生成的二维图像为输入,结合双流特征提取(DSFE)模块,提出了一种条件扩散模型对三维血管系统进行外推。DSFE模块由视觉转换器(Vision Transformer)和图形卷积网络(Graph Convolutional Network)组成,协同捕获全局连接合理性的视觉特征和局部血管细节的结构特征,保证生成三维数据的真实性和多样性。据我们所知,VesselDiffusion是第一个设计用于生成具有扩散过程的全面和逼真的血管网络的模型。与其他生成方法的对比分析表明,该框架具有较高的准确性和多样性。我们的代码可在:https://github.com/gzq17/VesselDiffusion。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VesselDiffusion: 3D Vascular Structure Generation Based on Diffusion Model
3D vascular structure models are pivotal in disease diagnosis, surgical planning, and medical education. The intricate nature of the vascular system presents significant challenges in generating accurate vascular structures. Constrained by the complex connectivity of the overall vascular structure, existing methods primarily focus on generating local or individual vessels. In this paper, we introduce a novel two-stage framework termed VesselDiffusion for the generation of detailed vascular structures, which is more valuable for medical analysis. Given that training data for specific vascular structure is often limited, direct generation of 3D data often results in inadequate detail and insufficient diversity. To this end, we initially train a 2D vascular generation model utilizing extensively available generic 2D vascular datasets. Taking the generated 2D images as input, a conditional diffusion model, integrating a dual-stream feature extraction (DSFE) module, is proposed to extrapolate 3D vascular systems. The DSFE module, comprising a Vision Transformer and a Graph Convolutional Network, synergistically captures visual features of global connection rationality and structural features of local vascular details, ensuring the authenticity and diversity of the generated 3D data. To the best of our knowledge, VesselDiffusion is the first model designed for generating comprehensive and realistic vascular networks with diffusion process. Comparative analyses with other generation methodologies demonstrate that the proposed framework achieves superior accuracy and diversity. Our code is available at: https://github.com/gzq17/VesselDiffusion.
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
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信