HiP-CT Enables Multiscale Segmentation With Deep Learning
Zhou et al., published February 2, 2026, present a deep learning pipeline that leverages Hierarchical Phase-Contrast Tomography (HiP-CT) multiscale scans to segment small functional units across whole organs. Trained on high-resolution VOIs and using pseudo-labels to extend predictions to ca. 25 /voxel whole-kidney scans, nnUNet achieved a test Dice of 0.906 and detected 1,019,890 and 231,179 glomeruli in two donors. The pipeline enables comprehensive 3D morphological and spatial analyses at organ scale.
Scoring Rationale
Significant methodological advance with validated whole-organ glomeruli mapping; peer-reviewed publication and open code enhance reproducibility and adoption.
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Sources
- Read OriginalMultiscale segmentation using hierarchical phase-contrast tomography and deep learningjournals.plos.org
