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

The Timing of Differentiation of Adult Hippocampal Neurons Is Crucial for Spatial Memory

Adult neurogenesis in the dentate gyrus plays a critical role in hippocampus-dependent spatial learning. It remains unknown, however, how new neurons become functionally integrated into spatial circuits and contribute to hippocampus-mediated forms of learning and memory. To investigate these issues, we used a mouse model in which the differentiation of adult-generated dentate gyrus neurons can be anticipated by conditionally expressing the pro-differentiative gene PC3 (Tis21/BTG2) in nestin-positive progenitor cells. In contrast to previous studies that affected the number of newly generated neurons, this strategy selectively changes their timing of differentiation. New, adult-generated dentate gyrus progenitors, in which the PC3 transgene was expressed, showed accelerated differentiation and significantly reduced dendritic arborization and spine density. Functionally, this genetic manipulation specifically affected different hippocampus-dependent learning and memory tasks, including contextual fear conditioning, and selectively reduced synaptic plasticity in the dentate gyrus. Morphological and functional analyses of hippocampal neurons at different stages of differentiation, following transgene activation within defined time-windows, revealed that the new, adult-generated neurons up to 3–4 weeks of age are required not only to acquire new spatial information but also to use previously consolidated memories. Thus, the correct unwinding of these key memory functions, which can be an expression of the ability of adult-generated neurons to link subsequent events in memory circuits, is critically dependent on the correct timing of the initial stages of neuron maturation and connection to existing circuits

Single and double grating-based X-ray microtomography using synchrotron radiation

Hard X-ray phase contrast imaging techniques have become most suitable for the non-destructive three-dimensional visualization of soft tissues at the microscopic level. Among the hard X-ray grating interferometry methods, a single-grating approach (XSGI) has been implemented by simplifying the established double-grating interferometer (XDGI). We quantitatively compare the XSGI and XDGI tomograms of a human nerve and demonstrate that both techniques provide sufficient contrast to allow for the distinction of tissue types. The two-fold binned data show spatial resolution of (5.2 ± 0.6) μm and (10.7 ± 0.6) μm, respectively, underlying the performance of XSGI in soft tissue imaging

Characterization of mechano-sensitive nano-containers for targeted vasodilation

Cardiovascular diseases are the worldwide number one cause of mortality. The blood flow in diseased human coronary arteries differs from the blood flow in the healthy vessels. This fact should be used for designing targeted localized delivery of vasodilators with a purely physical drug release trigger. Thus, we have proposed mechano-sensitive liposomes as mechano-sensitive containers. One has to tailor the liposome's properties, so that containers are stable under physiological conditions in health, but release their cargo near the constricted vessels at body temperature. In order to determine the shear stress threshold for release, both the morphology of the healthy and diseased human arteries and the mechanical property of the liposomes have to be known. We have shown that micro computed tomography (mu CT) techniques allow visualizing the lumen of human coronary arteries and provide the basis for flow simulations to extract the wall shear stress of healthy and stenosed regions in human coronary arteries. The behavior of the mechano-sensitive liposomes is currently investigated by means of microfluidics and spatially resolved small-angle X-ray scattering. The liposomes are injected into micro-channels mimicking in vivo situation. The scattering signal from the liposomes reveals information about their size, shape, and wall thickness

Automatic deformable registration of histological slides to {μCT} volume {3D}-Data

Localizing a histological section in the three‐dimensional dataset of a different imaging modality is a challenging 2D‐3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (μCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching μCT slice was off the ground‐truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 μm in jaw bone datasets, and the deformable approach improved registration by 33 μm with respect to the median distance error for four histological slides in the cerebellum dataset