Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney
This dataset addresses a critical bottleneck for researchers in biomedical image segmentation, enabling advancements in unsupervised, semi-supervised, and transfer learning, though it is incremental as it focuses on data provision rather than algorithmic innovation.
The authors tackled the lack of large-scale 3D biomedical datasets for machine learning by creating the HR-Kidney dataset, which provides 1.7 TB of high-quality mouse kidney images and validated segmentations of 33,729 glomeruli, representing a one to two orders of magnitude increase over existing datasets.
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.