{"title":"Comparing Two Bootstrapped Regions in Images: The D-Test","authors":"Florentin Kucharczak , Inés Couso , Olivier Strauss , Denis Mariano-Goulart","doi":"10.1016/j.irbm.2024.100821","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>Many molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available.</p></div><div><h3>Material and methods</h3><p>After presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance.</p></div><div><h3>Results</h3><p>The results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test.</p></div><div><h3>Conclusion</h3><p>This paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":"45 1","pages":"Article 100821"},"PeriodicalIF":5.6000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1959031824000022/pdfft?md5=8064e78cbcbc3824ad2b83a752908c7b&pid=1-s2.0-S1959031824000022-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1959031824000022","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives
Many molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available.
Material and methods
After presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance.
Results
The results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test.
Conclusion
This paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.
目标许多分子成像诊断都需要比较图像或不同图像中的两个感兴趣区(ROI)。由于图像是通过测量随机现象获得的,因此这种比较应基于统计检验以确保可靠性。最近的研究表明,使用引导法可以获得分子图像中重建值的统计变异性。然而,尽管人们普遍认为这种信息的增加应使基于分子图像的诊断更加可靠,但相关文献中还没有提出使用引导复制法来提高两个 ROI 比较的可靠性。在本文中,我们建议通过引入第一个统计检验来填补这一空白,该检验允许我们对两组像素/体素进行比较,而这两组像素/体素都有引导复制。最后,文章提出了两个基于定量比较和专家判断的实验来评估其相关性。结果所获得的结果与合成数据的专家诊断结果一致。结论本文提出了第一个统计检验方法,用于比较重建图像中的两个 ROI(可获得统计变异性信息)。
期刊介绍:
IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux).
As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in:
-Physiological and Biological Signal processing (EEG, MEG, ECG…)-
Medical Image processing-
Biomechanics-
Biomaterials-
Medical Physics-
Biophysics-
Physiological and Biological Sensors-
Information technologies in healthcare-
Disability research-
Computational physiology-
…