{"title":"半监督三维医学图像分割的双不确定度混合一致性","authors":"Chenchu Xu;Yuan Yang;Zhiqiang Xia;Boyan Wang;Dong Zhang;Yanping Zhang;Shu Zhao","doi":"10.1109/TBDATA.2023.3258643","DOIUrl":null,"url":null,"abstract":"3D semi-supervised medical image segmentation is extremely essential in computer-aided diagnosis, which can reduce the time-consuming task of performing annotation. The challenges with current 3D semi-supervised segmentation algorithms includes the methods, limited attention to volume-wise context information, their inability to generate accurate pseudo labels and a failure to capture important details during data augmentation. This article proposes a dual uncertainty-guided mixing consistency network for accurate 3D semi-supervised segmentation, which can solve the above challenges. The proposed network consists of a Contrastive Training Module which improves the quality of augmented images by retaining the invariance of data augmentation between original data and their augmentations. The Dual Uncertainty Strategy calculates dual uncertainty between two different models to select a more confident area for subsequent segmentation. The Mixing Volume Consistency Module that guides the consistency between mixing before and after segmentation for final segmentation, uses dual uncertainty and can fully learn volume-wise context information. Results from evaluative experiments on brain tumor and left atrial segmentation shows that the proposed method outperforms state-of-the-art 3D semi-supervised methods as confirmed by quantitative and qualitative analysis on datasets. This effectively demonstrates that this study has the potential to become a medical tool for accurate segmentation. Code is available at: \n<uri>https://github.com/yang6277/DUMC</uri>\n.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 4","pages":"1156-1170"},"PeriodicalIF":7.5000,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Dual Uncertainty-Guided Mixing Consistency for Semi-Supervised 3D Medical Image Segmentation\",\"authors\":\"Chenchu Xu;Yuan Yang;Zhiqiang Xia;Boyan Wang;Dong Zhang;Yanping Zhang;Shu Zhao\",\"doi\":\"10.1109/TBDATA.2023.3258643\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"3D semi-supervised medical image segmentation is extremely essential in computer-aided diagnosis, which can reduce the time-consuming task of performing annotation. The challenges with current 3D semi-supervised segmentation algorithms includes the methods, limited attention to volume-wise context information, their inability to generate accurate pseudo labels and a failure to capture important details during data augmentation. This article proposes a dual uncertainty-guided mixing consistency network for accurate 3D semi-supervised segmentation, which can solve the above challenges. The proposed network consists of a Contrastive Training Module which improves the quality of augmented images by retaining the invariance of data augmentation between original data and their augmentations. The Dual Uncertainty Strategy calculates dual uncertainty between two different models to select a more confident area for subsequent segmentation. The Mixing Volume Consistency Module that guides the consistency between mixing before and after segmentation for final segmentation, uses dual uncertainty and can fully learn volume-wise context information. Results from evaluative experiments on brain tumor and left atrial segmentation shows that the proposed method outperforms state-of-the-art 3D semi-supervised methods as confirmed by quantitative and qualitative analysis on datasets. This effectively demonstrates that this study has the potential to become a medical tool for accurate segmentation. Code is available at: \\n<uri>https://github.com/yang6277/DUMC</uri>\\n.\",\"PeriodicalId\":13106,\"journal\":{\"name\":\"IEEE Transactions on Big Data\",\"volume\":\"9 4\",\"pages\":\"1156-1170\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2023-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Big Data\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10075508/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10075508/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Dual Uncertainty-Guided Mixing Consistency for Semi-Supervised 3D Medical Image Segmentation
3D semi-supervised medical image segmentation is extremely essential in computer-aided diagnosis, which can reduce the time-consuming task of performing annotation. The challenges with current 3D semi-supervised segmentation algorithms includes the methods, limited attention to volume-wise context information, their inability to generate accurate pseudo labels and a failure to capture important details during data augmentation. This article proposes a dual uncertainty-guided mixing consistency network for accurate 3D semi-supervised segmentation, which can solve the above challenges. The proposed network consists of a Contrastive Training Module which improves the quality of augmented images by retaining the invariance of data augmentation between original data and their augmentations. The Dual Uncertainty Strategy calculates dual uncertainty between two different models to select a more confident area for subsequent segmentation. The Mixing Volume Consistency Module that guides the consistency between mixing before and after segmentation for final segmentation, uses dual uncertainty and can fully learn volume-wise context information. Results from evaluative experiments on brain tumor and left atrial segmentation shows that the proposed method outperforms state-of-the-art 3D semi-supervised methods as confirmed by quantitative and qualitative analysis on datasets. This effectively demonstrates that this study has the potential to become a medical tool for accurate segmentation. Code is available at:
https://github.com/yang6277/DUMC
.
期刊介绍:
The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.