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  3. Multiscale Segmentation Using Hierarchical Phase-contrast Tomography And Deep Learning.
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  3. Multiscale Segmentation Using Hierarchical Phase-contrast Tomography And Deep Learning.

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Multiscale segmentation using hierarchical phase-contrast tomography and deep learning.

Yang Zhou1, Shahab Aslani2,3, Yousef Javanmardi1

  • 1Multiscale X-ray Imaging (MXI) Lab, Department of Mechanical Engineering, University College London, London, United Kingdom.

Plos Computational Biology
|February 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a deep learning pipeline for multiscale biomedical image segmentation using Hierarchical Phase-Contrast Tomography (HiP-CT). The method effectively segments small functional units, like kidney glomeruli, across different resolutions for comprehensive organ analysis.

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Area of Science:

  • Biomedical imaging
  • Computational pathology
  • Deep learning for medical image analysis

Background:

  • Biomedical systems analysis requires integrating information across multiple spatial scales.
  • Existing image segmentation methods struggle with multiscale data, limiting analysis of small structures within whole organs.
  • Hierarchical Phase-Contrast Tomography (HiP-CT) provides 3D multiscale datasets, enabling high-resolution to whole-organ imaging.

Purpose of the Study:

  • To develop and validate a deep learning pipeline for multiscale biomedical image segmentation.
  • To enable the analysis of small functional units (e.g., glomeruli) across different resolutions within entire organs.
  • To apply the pipeline to human kidney glomeruli segmentation as a case study.

Main Methods:

  • Utilized Hierarchical Phase-Contrast Tomography (HiP-CT) to generate multiscale 3D datasets.
  • Developed a deep learning segmentation pipeline trained on high-resolution data and extended to lower resolutions using pseudo-labels and image registration.
  • Benchmarked four 3D deep learning models, selecting nnUNet as the baseline for its superior performance (Dice score 0.906).

    • Main Results:

    • The proposed pipeline successfully segmented over 1 million glomeruli in whole human kidneys at low resolution (approx. 25 µm/voxel).
    • Identified significant differences in glomerular counts between a healthy donor and a hypertensive patient.
    • Enabled comprehensive morphological analyses, including spatial statistics and distribution patterns, consistent with anatomical studies.

    Conclusions:

    • The developed deep learning pipeline effectively segments small functional units in multiscale bioimaging datasets.
    • HiP-CT combined with deep learning offers a powerful approach for comprehensive organ-level analysis.
    • The methodology shows broad applicability for segmenting various organ systems across different scales.