Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Germany.
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
Rofo. 2021 Mar;193(3):276-288. doi: 10.1055/a-1244-2775. Epub 2020 Nov 26.
The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets.
The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP.
First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria.
It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups.
· The DRG-ÖRG IRP is a web/cloud-based radiomics platform based on a public-private partnership.. · The DRG-ÖRG IRP can be used for the creation of quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis.. · First results show the applicability of left ventricular myocardial segmentation using a neural network and segment-based LGE detection using radiomic image features.. · The DRG-ÖRG IRP offers the possibility of integrating pre-trained neural networks and networking of scientific groups..
· Overhoff D, Kohlmann P, Frydrychowicz A et al. The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies. Fortschr Röntgenstr 2021; 193: 276 - 287.
DRG-ÖRG IRP(德国放射学会-奥地利放射学会国际放射组学平台)是基于公私合作伙伴关系的网络/云基础放射组学平台。它提供了在人工智能、放射组学分析和综合诊断领域进行数据共享、注释、验证和认证的可能性。在初步概念验证研究中,将使用放射组学图像特征自动进行心肌分割和自动心肌晚期钆增强(LGE)检测,以评估心肌炎数据集。
DRG-ÖRP IRP 可与临床数据结合使用,创建质量保证的结构化图像数据,并进行后续的综合数据分析,其特点如下:使用多中心联网数据的可能性、自动计算的质量参数、注释任务的处理、使用常规和人工智能方法进行轮廓识别以及有针对性地集成算法的可能性。在初步研究中,使用基于心脏 CINE 数据集预训练的神经网络评估 PSIR 数据集的分割。在第二步中,应用放射组学特征对同一数据集的 LGE 进行分段检测,这些数据集通过 IRP 进行多中心提供。
首先,该平台方法的优势(数据透明度、可靠性、所有成员的广泛参与、持续发展以及验证和认证)得到了证明。在概念验证研究中,神经网络与专家对心肌的分割相比,Dice 系数为 0.813。在基于节段的心肌 LGE 检测中,排除不确定注释的节段后,AUC 分别为 0.73 和 0.79。数据的评估和提供在 IRP 中进行,同时考虑了公平性、问责制、透明度(FAT)和可发现性、可访问性、互操作性、可重用性(FAIR)标准。
结果表明,DRG-ÖRP IRP 可以用作生成进一步的个体和联合项目的结晶点。通过 DRG-ÖRP IRP 的平台方法,人工智能方法的定量分析执行变得更加容易,因为可以集成预先训练的神经网络,并且可以将科学团队联网。在自动分割心肌和自动心肌 LGE 检测的初步概念验证研究中,这些优势得到了成功应用。我们的研究表明,通过使用 DRG-ÖRG IRP,可以以跨学科的方式实现战略目标,可以展示具体的概念验证示例,并且可以以参与的方式实现大量的个体和联合项目,所有团体都参与其中。
· DRG-ÖRG IRP 是一个基于公私合作伙伴关系的网络/云基础放射组学平台。
· DRG-ÖRG IRP 可用于创建与临床数据相结合的质量保证、结构化图像数据,以及随后的综合数据分析。
· 初步结果表明,使用神经网络进行左心室心肌分割和使用放射组学图像特征进行基于节段的 LGE 检测是可行的。
· DRG-ÖRG IRP 提供了集成预先训练的神经网络和科学团队联网的可能性。
· Overhoff D, Kohlmann P, Frydrychowicz A 等。国际放射组学平台 - 德国和奥地利放射学会的倡议。Fortschr Röntgenstr 2021; 193: 276-287.