The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks.

翻译：机器学习算法在三维生物医学图像分割中的表现远未达到基于二维照片所预期的水平。这可能是由于缺乏高质量、大规模的标注训练数据集——这类数据集需要最先进的成像设备、领域专家的标注工作以及大量计算资源与人力资源。本研究提出的HR-Kidney数据集填补了这一空白，提供了1.7 TB经伪影校正的全小鼠肾脏同步辐射X射线相位衬度显微断层扫描图像，以及33,729个肾小球的验证分割结果，较现有生物医学数据集规模提升一至两个数量级。该图像集还包含原始底层数据、基于阈值和形态学的肾血管及肾小管半自动分割结果，以及真实的三维人工标注。由此，我们为科学界在图像处理、数据增强和机器学习（特别是无监督与半监督学习研究，以及迁移学习和生成对抗网络）领域的发展与拓展奠定了广阔基础。