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

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 hierarchi

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC12880754 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature