Multiscale segmentation using hierarchical phase-contrast tomography and deep learning

PLoS Computational Biology, vol. 22, pp. e1013923

Abstract

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 [Formula: see text]/voxel to whole-organ scans at ca. 20 [Formula: see text]/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 [Formula: see text]/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 [Formula: see text]/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.

Authors 11

1. Yang Zhou

   University College London

   Affiliation as printed

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

2. Shahab Zeini Aslani

   University College London

   Affiliation as printed

   Department of Respiratory Medicine, University College London, London, United Kingdom

   Satsuma Lab, Hawkes Institute, University College London, London, United Kingdom

3. Yousef Javanmardi

   University College London

   Affiliation as printed

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

4. Joseph Brunet

   European Synchrotron Radiation Facility · University College London

   Affiliation as printed

   European Synchrotron Radiation Facility, Grenoble, France

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

5. David Stansby

   University College London

   Affiliation as printed

   Advanced Research Computing Centre, University College London, London, United Kingdom

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

6. Saskia Carroll

   University College London

   Affiliation as printed

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

7. Alexandre Bellier

   Inserm · Université Grenoble Alpes

   Affiliation as printed

   Univ. Grenoble Alpes, Department of Anatomy (LADAF), AGEIS, CIC INSERM, Grenoble, France

8. Maximilian Ackermann Aachen Institute of Pathology

   University Medical Center of the Johannes Gutenberg University Mainz · Helios Universitätsklinikum Wuppertal · Universitätsklinikum Aachen

   Affiliation as printed

   Institute of Anatomy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

   Institute of Pathology and Department of Molecular Pathology, Helios University Clinic Wuppertal, Wuppertal, Germany

   Institute of Pathology, Uniklinik RWTH Aachen, Aachen, Germany

9. Paul Tafforeau

   European Synchrotron Radiation Facility

   Affiliation as printed

   European Synchrotron Radiation Facility, Grenoble, France

10. Peter Lee

    University College London

    Affiliation as printed

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

11. Claire Walsh corresponding

    University College London

    Affiliation as printed

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