bioRxiv Science⌕ Search

Biology subjects

Javanmardi, Y.

Publications and source records attributed to Javanmardi, Y..

2 recordsLinked to original sources

A whole-organ multi-scale in silico framework for human kidney haemodynamics informed by hierarchical phase-contrast tomography

Studying human kidney haemodynamics has been limited by the absence of complete vascular maps of the whole organ. Here we utilise previously generated multi-resolution hierarchical phase-contrast tomography (HiP-CT) coupled with a hybrid anatomically-grounded synthetic reconstruction to generate a full arterial-glomerular network of an intact human kidney comprising 1.6 million vessels and over 800,000 glomeruli. Using this anatomically comprehensive structure, we apply physics-based zero-dimensional haemodynamic modelling to quantify blood pressure, flow and simulated filtration rate across the entire organ. We show that the reconstructed human kidney vascular network exhibits order-dependent branching behaviour similar to that of rat kidneys, and that physiologically plausible pressure and flow patterns are recovered only when the full vascular network is represented. We further demonstrate how the kidney responds to macrovascular and microvascular perturbations, including stenosis of the large renal arteries and narrowing or ablation of afferent arterioles. Stenosis and arteriole narrowing exhibit threshold-type behaviour, with kidney perfusion and simulated glomerular filtration rate remaining largely preserved up to [~]50% narrowing, followed by sharp nonlinear declines beyond [~]70%. These predictions emerge in the absence of autoregulatory mechanisms, indicating that vascular geometry and resistance scaling alone contribute to kidney functional deterioration. Together, our framework provides the first organ-wide, data-driven model of human kidney haemodynamics and offers a foundation for future studies of kidney physiology and disease.

bioengineering↗

Multiscale Segmentation using Hierarchical Phase-contrast Tomography and Deep Learning

Biomedical systems span multiple spatial scales, encompassing tiny functional units to entire organs. Interpreting these systems through image segmentation requires the effective propagation and integration of information across different scales. However, most existing segmentation methods are optimised for single-scale imaging modalities, limiting their ability to capture and analyse small functional units throughout complete human organs. To facilitate multiscale biomedical image segmentation, we utilised Hierarchical Phase-Contrast Tomography (HiP-CT), an advanced imaging modality that can generate 3D multiscale datasets from high-resolution volumes of interest (VOIs) at ca. 1 {micro}m/voxel to whole-organ scans at ca. 20 {micro}m/voxel. Building on these hierarchical multiscale datasets, we developed a deep learning-based segmentation pipeline that is initially trained on manually annotated high-resolution HiP-CT data and then extended to lower-resolution whole-organ scans using pseudo-labels generated from high-resolution predictions and multiscale image registration. As a case study, we focused on glomeruli in human kidneys, benchmarking four 3D deep learning models for biomedical image segmentation on a manually annotated high-resolution dataset extracted from VOIs, at 2.58 to ca. 5 {micro}m/voxel, of four human kidneys. Among them, nnUNet demonstrated the best performance, achieving an average test Dice score of 0.906, and was subsequently used as the baseline model for multiscale segmentation in the pipeline. Applying this pipeline to two low-resolution full-organ data at ca. 25 {micro}m/voxel, the model identified 1,019,890 and 231,179 glomeruli in a 62-year-old donor without kidney diseases and a 94-year-old hypertensive donor, enabling comprehensive morphological analyses, including cortical spatial statistics and glomerular distributions, which aligned well with previous anatomical studies. Our results highlight the effectiveness of the proposed pipeline for segmenting small functional units in multiscale bioimaging datasets and suggest its broader applicability to other organ systems.

bioinformatics↗