Biology subjects

Kjer, H. M.

Publications and source records attributed to Kjer, H. M..

6 recordsLinked to original sources

bioRxiv2026

Mapping 3D tissue orientation with tensor-augmented light-sheet microscopy

Mapping the three-dimensional directional organization of biological tissues is essential for understanding their structure and function. However, existing methods cannot resolve micrometer-scale orientation in volumetric samples. We introduce tensor light-sheet scattering microscopy (tLSSM), a label-free method that reconstructs 3D fiber orientations at micrometer resolution in optically cleared tissues, enabling whole-organ imaging. We discovered that even in transparent samples, organized structures such as neural fibers scatter light in directional patterns, consistent with models of light scattering by cylinders. We compared tLSSM against diffusion MRI in the mouse brain, demonstrating strong agreement and orders of magnitude superior spatial resolution. Furthermore, we showcase tLSSMs versatility across diverse contexts, including whole-brain label-free tracing, pathological demyelination lesions, heart tissue, peripheral nerves, and human white matter. By enabling whole-organ fiber orientation mapping compatible with standard light-sheet microscopes, tLSSM establishes a new standard for mesoscopic connectivity studies by mapping tissue architecture beyond the limits of traditional sectioning.

neuroscience↗

bioRxiv2025

From Few Labels to Full Insight: 3D Semantic Segmentation of Brain White Matter Microstructures from a Sparsely Annotated X-ray Nano Holotomography Dataset

Morphological analysis of white matter (WM) microstructures provides invaluable insights into its function and brain health overall. Recently, synchrotron X-ray Nano Holography (XNH) imaging has gained momentum in microstructural studies owing to its unique combination of capabilities, including a wide field of view (FOV), nanoscale resolution, and fast volumetric acquisition. However, the 3D segmentation of tiny and densely packed microstructures within the WM, the very first step toward quantification, presents a significant bottleneck. Herein, we address this challenge using an expert-in-the-loop 3D deep learning (DL) framework. The initial model was a 3D U-Net architecture trained on sparsely annotated data, exclusively from the corpus callosum (CC), a region characterized by relatively simple axonal morphology and parallel orientation of axonal bundles. The high-confidence predictions of the model (p > 0.7) from both CC and the complex crossing fiber regions are then refined by an expert and reintegrated into the training process to expand the training dataset and the corresponding labels. Building upon this foundation, we benchmarked alternative architectures, including 3D convolutional neural networks (CNN) and transformer-based architectures, alongside different loss functions. We showcased the segmentation performance of the models in XNH volumes with varying microstructural morphology and organization. All DL architectures achieved satisfactory performance with an overall segmentation accuracy > 0.96 and an average Dice score > 0.8 in 3D segmentation of axons, blood vessels, cells, and vacuoles. This study presents a multi-class 3D segmentation of WM microstructures in large FOVs up to [~]202 x 202 x 780 {micro}m3 scanned through XNH technology. Our framework not only accelerates high-fidelity segmentation in challenging datasets but also paves the way for fast quantitative analysis in large-scale 3D neuroimaging studies.

neuroscience↗

bioRxiv2023

Exploring white matter dynamics and morphology through interactive numerical phantoms: The White Matter Generator

Brain white matter is a dynamic environment that continuously adapts and reorganizes in response to stimuli and pathological changes. Glial cells, especially, play a key role in tissue repair, inflammation modulation, and neural recovery. The movements of glial cells and changes in their concentrations can influence the surrounding axon morphology. We introduce the White Matter Generator (WMG) tool to enable the study of how axon morphology is influenced through such dynamical processes, and how this, in turn, influences the diffusion-weighted MRI signal. This is made possible by allowing interactive changes to the configuration of the phantom generation throughout the optimisation process. The phantoms can consist of axons, myelinated axons, and cell clusters, separated by extra-cellular space. Due to morphological flexibility and computational advantages during the optimisation, the tool uses ellipsoids as building blocks for all structures; chains of ellipsoids for axons, and individual ellipsoids for cell clusters. After optimisation, the ellipsoid representation can be converted to a mesh representation which can be employed in Monte-Carlo diffusion simulations. This offers an effective method for evaluating tissue microstructure models for diffusion-weighted MRI in controlled realistic white matter environments. Hence, the WMG offers valuable insights into white matters adaptive nature and implications for diffusion-weighted MRI microstructure models, and thereby holds the potential to advance clinical diagnosis, treatment, and rehabilitation strategies for various neurological disorders and injuries.

neuroscience↗

bioRxiv2021

Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

Noninvasive estimation of axon diameter with diffusion MRI holds potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent methods use powder averaging to account for complex white matter architectures, such as fibre crossing regions, but these have not been validated for real axonal geometries. Here, we present 120 - 313 m long segmented axons from X-ray nano-holotomography volumes of a splenium and crossing fibre region of a vervet monkey brain. We show that the axons in the complex crossing fibre region, which contains callosal, association, and corticospinal connections, are larger and exhibit a wider distribution than those of the splenium region. To accurately estimate the axon diameter in these regions, therefore, sensitivity to a wide range of diameters is required. We demonstrate how the q-value, b-value, signal-to-noise ratio and the assumed intra-axonal parallel diffusivity influence the range of measurable diameters with powder average approaches. Furthermore, we show how Gaussian distributed noise results in a wider range of measurable diameter at high b-values than Rician distributed noise, even at high signal-to-noise ratios of 100. The number of gradient directions is also shown to impose a lower bound on measurable diameter. Our results indicate that axon diameter estimation can be performed with only few b-shells, and that additional shells do not improve the accuracy of the estimate. Through Monte Carlo simulations of diffusion, we show that powder averaging techniques succeed in providing accurate estimates of axon diameter across a range of sequence parameters and diffusion times, even in complex white matter architectures. At sufficiently low b-values, the acquisition becomes sensitive to axonal microdispersion and the intra-axonal parallel diffusivity shows time dependency at both in vivo and ex vivo intrinsic diffusivities.

neuroscience↗

bioRxiv2020

InSegtCone: Interactive Segmentation of crystalline Cones in compound eyes

Understanding the diversity of eyes is crucial to unravel how different animals use vision to interact with their respective environments. To date, comparative studies of eye anatomy are scarce because they often involve time-consuming or inefficient methods. X-ray micro-tomography is a promising high-throughput imaging technique that enables to reconstruct the 3D anatomy of eyes, but powerful tools are needed to perform fast conversions of anatomical reconstructions into functional eye models. We developed a computing method named InSegtCone to automatically segment the crystalline cones in the apposition compound eyes of arthropods. Here, we describe the full auto-segmentation process, showcase its application to three different insect compound eyes and evaluate its performance. The auto-segmentation could successfully label the full individual shapes of 60%-80% of the crystalline cones, and is about as accurate and 250 times faster than manual labelling of the individual cones. We believe that InSegtCone can be an important tool for peer scientists to enable extensive comparisons of the diversity of eyes and vision in arthropods.

zoology↗

bioRxiv2020

Axon morphology is modulated by the local environment and impacts the non-invasive investigation of its structure-function relationship

Axonal conduction velocity, which ensures efficient function of the brain network, is related to axon diameter. Non-invasive, in vivo axon diameter estimates can be made with diffusion magnetic resonance imaging, but the technique requires 3D validation. Here, high resolution, 3D synchrotron X-ray Nano-Holotomography images of white matter samples from the corpus callosum of a monkey brain reveal that blood vessels, cells and vacuoles affect axonal diameter and trajectory. Within single axons, we find that the variance in diameter and conduction velocity correlates with the mean diameter, contesting the value of precise diameter determination in larger axons. These complex 3D axon morphologies drive previously reported 2D trends in axon diameter and g-ratio. Furthermore, we find that these morphologies bias the estimates of axon diameter with diffusion magnetic resonance imaging and, ultimately, impact the investigation and formulation of the axon structure-function relationship.

neuroscience↗