惰的月亮:可扩展的软件,用于心血管磁共振成像中的多层阅读器比较
Thomas Hadler1, Clemens Ammann2, Jens Wetzl3
1Working Group on CMR, Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité - Universitätsmedizin Berlin, Berlin, Germany; Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany.
惰的月亮软件可以通过临床医生和AI进行心血管磁共振 (CMR) 图像分析的自动比较. 该工具简化了评估,并确定了定量参数变化的来源.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 软件开发 软件开发
背景情况:
- 心血管磁共振 (CMR) 影像在临床环境中越来越多地使用.
- 在CMR中,定量参数是从读者注释 (自然智能或人工智能) 中得出的.
- 评估这些定量参数的精度和可重复性对于临床应用至关重要.
研究的目的:
- 开发Lazy Luna (LL),一个灵活可扩展的软件工具,用于评估CMR成像中的定量参数.
- 简化人类读者和人工智能算法对注释的比较和分析.
- 为了促进新的成像序列和临床参数的整合.
主要方法:
- LL具有用于比较注释CMR图像的界面,确保使用几何物体进行精确的计算.
- 图形用户界面 (GUI) 使非程序员可以访问软件,以进行交互式检查和追踪注释差异.
- 后端使用元类,允许敏捷开发和扩展到新的成像序列和参数,并提供临床反.
主要成果:
- 在两个案例中,LL证明了可扩展性:T1参数映射,专家读者对图像进行细分,以及Emidec对AI在Late Gadolinium Enhancement (LGE) 量化中的开源挑战.
- 在T1映射中的定量差异被追溯到特定的细分差异,展示了LL的分析能力.
- 艾米德克挑战强调了LL作为人工智能开发开源软件的可用性.
结论:
- 惰的月亮在CMR图像分析中促进了阅读器性能的自动化,多层次的比较.
- 该软件识别了数量结果的统计变化背后的定性原因.
- LL是一个可扩展的开源工具,在各种CMR场景中具有未来应用的潜力.
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