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Updated: Jun 7, 2025
Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
Published on: July 26, 2014
Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms
J Báskay1, M Kivovics2, D Pénzes2
1Department of Biological Physics, Eötvös Loránd University, Pázmány Péter Sétány 1/a, 1117, Budapest, Hungary.
Background:
Histomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to deduce the spatial properties of structures. Micromorphometric parameters are calculated from these sections based on the assumption of a plate-like 3D microarchitecture, resulting in the loss of 3D structure due to the destructive nature of classical histological processing.
Materials And Methods:
To overcome the limitation of histomorphometry and reconstruct the 3D architecture of bone core biopsy samples from 2D histological sections, bone core biopsy samples were decalcified and embedded in paraffin. Subsequently, 5 µm thick serial sections were stained with hematoxylin and eosin and scanned using a 3DHISTECH PANNORAMIC 1000 Digital Slide Scanner (3DHISTECH, Budapest, Hungary). A modified U‑Net architecture was trained to categorize tissues on the sections. LoFTR feature matching combined with affine transformations was employed to create the histologic reconstruction. Micromorphometric parameters were calculated using Bruker's CTAn software (v. 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets.
Results:
Our method achieved an overall accuracy of 95.26% (95% confidence interval (CI): [94.15%, 96.37%]) with an F‑score of 0.9320 (95% CI: [0.9211, 0.9429]) averaged across all classes. Correlation coefficients between micromorphometric parameters measured on microCT imaging and histological reconstruction showed a strong linear relationship, with Spearman's ρ‑values of 0.777, 0.717, 0.705, 0.666, and 0.687 for bone volume/tissue volume (BV/TV), bone surface/TV, trabecular pattern factor, trabecular thickness, and trabecular separation, respectively. Bland-Altman and mountain plots indicated good agreement between the methods for BV/TV measurements.
Conclusion:
This method enables examination of tissue microarchitecture in 3D with an even higher resolution than microcomputed tomography (microCT), without losing information on cellularity. However, the limitation of this procedure is its destructive nature, which precludes subsequent mechanical testing of the sample or any further secondary measurements. Furthermore, the number of histological sections that can be created from a single sample is limited.
