In the rapidly evolving field of medical imaging, machine learning algorithms have become indispensable for enhancing diagnostic accuracy. However, the effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be facilitated in machine learning algorithms. This paper introduces an innovative data curation tool, developed as part of the Kaapana open-source toolkit, aimed at streamlining the organization, management, and processing of large-scale medical imaging datasets. The tool is specifically tailored to meet the needs of radiologists and machine learning researchers. It incorporates advanced search, auto-annotation and efficient tagging functionalities for improved data curation. Additionally, the tool facilitates quality control and review, enabling researchers to validate image and segmentation quality in large datasets. It also plays a critical role in uncovering potential biases in datasets by aggregating and visualizing metadata, which is essential for developing robust machine learning models. Furthermore, Kaapana is integrated within the Radiological Cooperative Network (RACOON), a pioneering initiative aimed at creating a comprehensive national infrastructure for the aggregation, transmission, and consolidation of radiological data across all university clinics throughout Germany. A supplementary video showcasing the tool's functionalities can be accessed at https://bit.ly/MICCAI-DEMI2023.

翻译：在医学影像快速发展的领域中，机器学习算法已成为提升诊断准确性的关键工具。然而，这些算法的有效性高度依赖于高质量医学影像数据集的可用性与组织规范性。传统的医疗数字影像传输（DICOM）数据管理系统难以满足机器学习算法所需数据的规模与复杂性要求。本文介绍了一种创新型数据整理工具，该工具作为Kaapana开源工具包的一部分开发，旨在简化大规模医学影像数据集的组织、管理与处理流程。该工具专门针对放射科医师与机器学习研究人员的需求设计，集成了高级检索、自动标注及高效标签功能以优化数据整理。此外，工具支持质量评估与审查，使研究人员能够验证大规模数据集中图像与分割标注的质量，并通过聚合与可视化元数据，在识别数据集潜在偏差方面发挥关键作用——这对构建鲁棒的机器学习模型至关重要。Kaapana已集成至放射学合作网络（RACOON），该网络是一项旨在为德国所有大学医院建立放射学数据聚合、传输与整合国家综合性基础设施的开创性项目。工具功能演示视频可通过 https://bit.ly/MICCAI-DEMI2023 访问。