{"title":"uRP: An integrated research platform for one-stop analysis of medical images.","authors":"Jiaojiao Wu, Yuwei Xia, Xuechun Wang, Ying Wei, Aie Liu, Arun Innanje, Meng Zheng, Lei Chen, Jing Shi, Liye Wang, Yiqiang Zhan, Xiang Sean Zhou, Zhong Xue, Feng Shi, Dinggang Shen","doi":"10.3389/fradi.2023.1153784","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.</p><p><strong>Methods: </strong>We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.</p><p><strong>Results and discussion: </strong>The uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365282/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in radiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fradi.2023.1153784","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction: Medical image analysis is of tremendous importance in serving clinical diagnosis, treatment planning, as well as prognosis assessment. However, the image analysis process usually involves multiple modality-specific software and relies on rigorous manual operations, which is time-consuming and potentially low reproducible.
Methods: We present an integrated platform - uAI Research Portal (uRP), to achieve one-stop analyses of multimodal images such as CT, MRI, and PET for clinical research applications. The proposed uRP adopts a modularized architecture to be multifunctional, extensible, and customizable.
Results and discussion: The uRP shows 3 advantages, as it 1) spans a wealth of algorithms for image processing including semi-automatic delineation, automatic segmentation, registration, classification, quantitative analysis, and image visualization, to realize a one-stop analytic pipeline, 2) integrates a variety of functional modules, which can be directly applied, combined, or customized for specific application domains, such as brain, pneumonia, and knee joint analyses, 3) enables full-stack analysis of one disease, including diagnosis, treatment planning, and prognosis assessment, as well as full-spectrum coverage for multiple disease applications. With the continuous development and inclusion of advanced algorithms, we expect this platform to largely simplify the clinical scientific research process and promote more and better discoveries.
引言医学图像分析在临床诊断、治疗计划和预后评估方面具有重要意义。然而,图像分析过程通常涉及多种特定模式的软件,并依赖于严格的手工操作,耗时且可重复性可能较低:我们提出了一个集成平台--uAI Research Portal(uRP),以实现临床研究应用中对 CT、MRI 和 PET 等多模态图像的一站式分析。uRP采用模块化架构,具有多功能性、可扩展性和可定制性:uRP具有三大优势:1)横跨半自动划线、自动分割、配准、分类、定量分析、图像可视化等丰富的图像处理算法,实现一站式分析流水线;2)集成多种功能模块,可直接应用、组合或定制特定应用领域的功能模块,如脑、肺炎、膝关节分析等;3)实现一种疾病的全栈分析,包括诊断、治疗计划和预后评估,以及多种疾病应用的全谱覆盖。随着先进算法的不断发展和加入,我们期待该平台能在很大程度上简化临床科研流程,促进更多更好的发现。