  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.
