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The performance of machine learning algorithms, when used for segmenting 3Dbiomedical images, does not reach the level expected based on results achievedwith 2D photos. This may be explained by the comparative lack of high-volume,high-quality training datasets, which require state-of-the-art imagingfacilities, domain experts for annotation and large computational and personalresources. The HR-Kidney dataset presented in this work bridges this gap byproviding 1.7 TB of artefact-corrected synchrotron radiation-based X-rayphase-contrast microtomography images of whole mouse kidneys and validatedsegmentations of 33 729 glomeruli, which corresponds to a one to two orders ofmagnitude increase over currently available biomedical datasets. The image setsalso contain the underlying raw data, threshold- and morphology-basedsemi-automatic segmentations of renal vasculature and uriniferous tubules, aswell as true 3D manual annotations. We therewith provide a broad basis for thescientific community to build upon and expand in the fields of imageprocessing, data augmentation and machine learning, in particular unsupervisedand semi-supervised learning investigations, as well as transfer learning andgenerative adversarial networks.

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney