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

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

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

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

Tomographic imaging of microvasculature with a purpose-designed, polymeric x-ray contrast agent

Imaging of microvasculature is primarily performed with X-ray contrast agents, owing to the wide availability of absorption-contrast laboratory source µCT compared to phase contrast capable devices. Standard commercial contrast agents used in angiography are not suitable for high-resolution imaging ex vivo, however, as they are small molecular compounds capable of diffusing through blood vessel walls within minutes. Large nanoparticle-based blood pool contrast agents on the other hand exhibit problems with aggregation, resulting in clogging in the smallest blood vessels. Injection with solidifying plastic resins has, therefore, remained the gold standard for microvascular imaging, despite the considerable amount of training and optimization needed to properly perfuse the viscous compounds. Even with optimization, frequent gas and water inclusions commonly result in interrupted vessel segments. This lack of suitable compounds has led us to develop the polymeric, cross-linkable X-ray contrast agent XlinCA. As a water-soluble organic molecule, aggregation and inclusions are inherently avoided. High molecular weight allows it to be retained even in the highly fenestrated vasculature of the kidney filtration system. It can be covalently crosslinked using the same aldehydes used in tissue fixation protocols, leading to stable and permanent contrast. These properties allowed us to image whole mice and individual organs in 6 to 12-month-old C57BL/6J mice without requiring lengthy optimizations of injection rates and pressures, while at the same time achieving greatly improved filling of the vasculature compared to resin-based vascular casting. This work aims at illuminating the rationales, processes and challenges involved in creating this recently developed contrast agent

FEASIBILITY STUDY OF SYNCHROTRON-BASED MICROTOMOGRAPHY TO IDENTIFY α-SYNUCLEIN OLIGOMERS IN POSTMORTEM TISSUE

Background Lewy body disease (LBD) is the second most common cause of neurodegenerative dementia after Alzheimer’s disease and presents a challenge to healthcare services in an ageing society because current therapeutic approaches do not halt the disease’s progress. It has been suggested that aggregation of the protein α-synuclein plays a critical role in LBD. One pathway for aggregation involves the creation of low molecular weight α-synuclein oligomers, which have been observed to induce cell death. Precise location of Lewy bodies and their precursors could allow researchers to better relate their presence to clinical symptoms as well as infer their physiological effects. While conventional light microscopy of histological slices can visualize Lewy bodies and larger protein aggregates, it provides only two-dimensional information and diffraction-limited resolution prevents visualization of the smaller (approximately 50 nanometer) protein bundles. For this reason, we propose hard X-ray micro- and nano-tomography modalities for complementary, non-destructive, three-dimensional visualization of brain tissues taking advantage of developments at synchrotron radiation facilities. Methods As a proof-of-concept, we imaged paraffin-embedded fusiform gyrus tissue (provided by Newcastle Brain Tissue Resource) from a DLB case at the European Synchrotron Radiation Facility (ESRF Grenoble, France provided beamtime at ID19 from proposal MD-1055) using single-distance inline phase-contrast X-ray tomography with pixel size of 1.6 micrometers. A H&E stained histology slice of the same sample was also taken. Results A correspondence between the micro computed tomography and histology datasets could be made by tracking the orientation of vessels in the specimen. The micro computed tomography demonstrated as shown in figure (left) sufficient contrast and resolution for identification of the lower cortical layer as well as visualization of cells in three-dimensional manner, with confirmation from the annotated histology slice. Conclusions The present work serves as a proof-of-concept for the complementary use of phase contrast X-ray tomography and histology in the investigation of neurodegenerative diseases down to the cellular level. These results open the door for further nanometer resolution studies. Opens large image Nearby slices from phase-contrast microcomputed tomography (μ-CT) (left) and H&E stained histology (right) of diseased human brain tissue. The modalities can be used in complement, with the non-destructive μ-CT directing cutting planes for histological sectioning and extending histology into the third dimension