用于非配对跨模态医学图像分割的三维解剖学引导自我训练分割框架

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yuzhou Zhuang;Hong Liu;Enmin Song;Xiangyang Xu;Yongde Liao;Guanchao Ye;Chih-Cheng Hung
{"title":"用于非配对跨模态医学图像分割的三维解剖学引导自我训练分割框架","authors":"Yuzhou Zhuang;Hong Liu;Enmin Song;Xiangyang Xu;Yongde Liao;Guanchao Ye;Chih-Cheng Hung","doi":"10.1109/TRPMS.2023.3332619","DOIUrl":null,"url":null,"abstract":"Unsupervised domain adaptation (UDA) methods have achieved promising performance in alleviating the domain shift between different imaging modalities. In this article, we propose a robust two-stage 3-D anatomy-guided self-training cross-modality segmentation (ASTCMSeg) framework based on UDA for unpaired cross-modality image segmentation, including the anatomy-guided image translation and self-training segmentation stages. In the translation stage, we first leverage the similarity distributions between patches to capture the latent anatomical relationships and propose an anatomical relation consistency (ARC) for preserving the correct anatomical relationships. Then, we design a frequency domain constraint to enforce the consistency of important frequency components during image translation. Finally, we integrate the ARC and frequency domain constraint with contrastive learning for anatomy-guided image translation. In the segmentation stage, we propose a context-aware anisotropic mesh network for segmenting anisotropic volumes in the target domain. Meanwhile, we design a volumetric adaptive self-training method that dynamically selects appropriate pseudo-label thresholds to learn the abundant label information from unlabeled target volumes. Our proposed method is validated on the cross-modality brain structure, cardiac substructure, and abdominal multiorgan segmentation tasks. Experimental results show that our proposed method achieves state-of-the-art performance in all tasks and significantly outperforms other 2-D based or 3-D based UDA methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 1","pages":"33-52"},"PeriodicalIF":4.6000,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A 3-D Anatomy-Guided Self-Training Segmentation Framework for Unpaired Cross-Modality Medical Image Segmentation\",\"authors\":\"Yuzhou Zhuang;Hong Liu;Enmin Song;Xiangyang Xu;Yongde Liao;Guanchao Ye;Chih-Cheng Hung\",\"doi\":\"10.1109/TRPMS.2023.3332619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unsupervised domain adaptation (UDA) methods have achieved promising performance in alleviating the domain shift between different imaging modalities. In this article, we propose a robust two-stage 3-D anatomy-guided self-training cross-modality segmentation (ASTCMSeg) framework based on UDA for unpaired cross-modality image segmentation, including the anatomy-guided image translation and self-training segmentation stages. In the translation stage, we first leverage the similarity distributions between patches to capture the latent anatomical relationships and propose an anatomical relation consistency (ARC) for preserving the correct anatomical relationships. Then, we design a frequency domain constraint to enforce the consistency of important frequency components during image translation. Finally, we integrate the ARC and frequency domain constraint with contrastive learning for anatomy-guided image translation. In the segmentation stage, we propose a context-aware anisotropic mesh network for segmenting anisotropic volumes in the target domain. Meanwhile, we design a volumetric adaptive self-training method that dynamically selects appropriate pseudo-label thresholds to learn the abundant label information from unlabeled target volumes. Our proposed method is validated on the cross-modality brain structure, cardiac substructure, and abdominal multiorgan segmentation tasks. Experimental results show that our proposed method achieves state-of-the-art performance in all tasks and significantly outperforms other 2-D based or 3-D based UDA methods.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":\"8 1\",\"pages\":\"33-52\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10317880/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10317880/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

无监督领域适应(UDA)方法在缓解不同成像模式之间的领域偏移方面取得了可喜的成绩。在本文中,我们提出了一种基于 UDA 的鲁棒两阶段三维解剖引导自我训练跨模态分割(ASTCMSeg)框架,用于无配对跨模态图像分割,包括解剖引导图像平移和自我训练分割阶段。在平移阶段,我们首先利用斑块间的相似性分布来捕捉潜在的解剖关系,并提出一种解剖关系一致性(ARC)来保留正确的解剖关系。然后,我们设计了一种频域约束,在图像翻译过程中强制执行重要频率成分的一致性。最后,我们将 ARC 和频域约束与对比学习相结合,实现解剖引导的图像翻译。在分割阶段,我们提出了一种情境感知各向异性网状网络,用于分割目标域中的各向异性体积。同时,我们还设计了一种体积自适应自我训练方法,可动态选择适当的伪标签阈值,从未标明的目标体积中学习丰富的标签信息。我们提出的方法在跨模态大脑结构、心脏亚结构和腹部多器官分割任务中得到了验证。实验结果表明,我们提出的方法在所有任务中都达到了最先进的性能,并明显优于其他基于二维或三维的 UDA 方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A 3-D Anatomy-Guided Self-Training Segmentation Framework for Unpaired Cross-Modality Medical Image Segmentation
Unsupervised domain adaptation (UDA) methods have achieved promising performance in alleviating the domain shift between different imaging modalities. In this article, we propose a robust two-stage 3-D anatomy-guided self-training cross-modality segmentation (ASTCMSeg) framework based on UDA for unpaired cross-modality image segmentation, including the anatomy-guided image translation and self-training segmentation stages. In the translation stage, we first leverage the similarity distributions between patches to capture the latent anatomical relationships and propose an anatomical relation consistency (ARC) for preserving the correct anatomical relationships. Then, we design a frequency domain constraint to enforce the consistency of important frequency components during image translation. Finally, we integrate the ARC and frequency domain constraint with contrastive learning for anatomy-guided image translation. In the segmentation stage, we propose a context-aware anisotropic mesh network for segmenting anisotropic volumes in the target domain. Meanwhile, we design a volumetric adaptive self-training method that dynamically selects appropriate pseudo-label thresholds to learn the abundant label information from unlabeled target volumes. Our proposed method is validated on the cross-modality brain structure, cardiac substructure, and abdominal multiorgan segmentation tasks. Experimental results show that our proposed method achieves state-of-the-art performance in all tasks and significantly outperforms other 2-D based or 3-D based UDA methods.
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
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
×
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