Yongsheng Pan, Yiwen Ye, Yanning Zhang, Yong Xia, Dinggang Shen
{"title":"Draw Sketch, Draw Flesh: Whole-Body Computed Tomography from Any X-Ray Views","authors":"Yongsheng Pan, Yiwen Ye, Yanning Zhang, Yong Xia, Dinggang Shen","doi":"10.1007/s11263-024-02286-2","DOIUrl":null,"url":null,"abstract":"<p>Stereoscopic observation is a common foundation of medical image analysis and is generally achieved by 3D medical imaging based on settled scanners, such as CT and MRI, that are not as convenient as X-ray machines in some flexible scenarios. However, X-ray images can only provide perspective 2D observation and lack view in the third dimension. If 3D information can be deduced from X-ray images, it would broaden the application of X-ray machines. Focus on the above objective, this paper dedicates to the generation of pseudo 3D CT scans from non-parallel 2D perspective X-ray (PXR) views and proposes the <i>Draw Sketch and Draw Flesh</i> (DSDF) framework to first roughly predict the tissue distribution (Sketch) from PXR views and then render the tissue details (Flesh) from the tissue distribution and PXR views. Different from previous studies that focus only on partial locations, e.g., chest or neck, this study theoretically investigates the feasibility of head-to-leg reconstruction, i.e., generally applicable to any body parts. Experiments on 559 whole-body samples from 4 cohorts suggest that our DSDF can reconstruct more reasonable pseudo CT images than state-of-the-art methods and achieve promising results in both visualization and various downstream tasks. The source code and well-trained models are available a https://github.com/YongshengPan/WholeBodyXraytoCT.</p>","PeriodicalId":13752,"journal":{"name":"International Journal of Computer Vision","volume":"26 1","pages":""},"PeriodicalIF":11.6000,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11263-024-02286-2","RegionNum":2,"RegionCategory":"计算机科学","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
Stereoscopic observation is a common foundation of medical image analysis and is generally achieved by 3D medical imaging based on settled scanners, such as CT and MRI, that are not as convenient as X-ray machines in some flexible scenarios. However, X-ray images can only provide perspective 2D observation and lack view in the third dimension. If 3D information can be deduced from X-ray images, it would broaden the application of X-ray machines. Focus on the above objective, this paper dedicates to the generation of pseudo 3D CT scans from non-parallel 2D perspective X-ray (PXR) views and proposes the Draw Sketch and Draw Flesh (DSDF) framework to first roughly predict the tissue distribution (Sketch) from PXR views and then render the tissue details (Flesh) from the tissue distribution and PXR views. Different from previous studies that focus only on partial locations, e.g., chest or neck, this study theoretically investigates the feasibility of head-to-leg reconstruction, i.e., generally applicable to any body parts. Experiments on 559 whole-body samples from 4 cohorts suggest that our DSDF can reconstruct more reasonable pseudo CT images than state-of-the-art methods and achieve promising results in both visualization and various downstream tasks. The source code and well-trained models are available a https://github.com/YongshengPan/WholeBodyXraytoCT.
期刊介绍:
The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs.
Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision.
Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community.
Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas.
In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives.
The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research.
Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.