A deep learning-based framework for standardized analysis of trabecular bone compartments from micro-CT imaging data in the mouse tibia

TL;DR

This paper introduces a deep learning framework for standardized analysis of mouse tibia trabecular bone compartments using micro-CT scans, improving reproducibility in skeletal research.

Contribution

A novel deep learning-based framework for automated and standardized analysis of mouse tibia trabecular bone compartments using micro-CT data.

Findings

The classification model achieved high F1-scores (0.96-0.99) across four trabecular compartments.

The framework demonstrated strong generalizability on an external dataset with consistent performance.

Automated segmentation and analysis of trabecular compartments enable reliable comparisons across experimental groups.

Abstract

Understanding bone remodeling and disease progression is crucial in preclinical skeletal research, particularly for assessing pharmacological and mechanical interventions in the long bones of murine models. High-resolution micro-computed tomography (micro-CT) imaging enables detailed trabecular bone analysis; however, inconsistent and non-standardized definitions of the volumes of interest (VOIs) across the different trabecular compartments compromise reproducibility and may lead to misleading statistical interpretations. In this study, we introduce a deep learning framework for automated trabecular bone analysis from micro-CT scans (5 µm voxel size) of the epiphyseal-metaphyseal region in the mouse tibia. The epiphyseal-metaphyseal region is classified into four anatomical compartments, epiphyseal bone, growth plate, primary spongiosa, and secondary spongiosa, using a 2D slice-wise classification model combined with a regional probability distribution method to detect the transitional landmarks between these compartments and enable standardized VOI extraction. To validate our method, we trained and tested the model on three micro-CT datasets comprising a total of 40 bone scans, each annotated by three experts to assess inter- and intra-operator variability, and further assessed its generalizability using an additional external dataset. These datasets encompassed diverse experimental conditions, including pharmacological treatments, mechanical loading, and age-related reduced bone density. Our classification model achieved excellent performances (mean F1-score of 0.96 for the epiphyseal bone, 0.95 for the growth plate, 0.92 for the primary spongiosa, and 0.99 for the secondary spongiosa across all datasets; statistical equivalence within 0.05 mm, \documentclass[12pt]{minimal}
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\begin{document}$$p \le 0.05$$\end{document}) and demonstrated strong generalizability on the external dataset (mean F1-score of 0.99 for the epiphyseal bone, 0.97 for the growth plate, 0.92 for the primary spongiosa, and 1.0 for the secondary spongiosa; statistical equivalence within 0.05 mm, \documentclass[12pt]{minimal}
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\begin{document}$$p \le 0.05$$\end{document}). Following the extraction of the different trabecular compartments, we segmented the trabecular bone within the epiphyseal bone, primary spongiosa, and secondary spongiosa using a deep learning-based segmentation model. We performed a comprehensive morphological and statistical analysis of all trabecular compartments in the mouse tibia, facilitating consistent comparisons across experimental groups and enabling direct comparisons within and between trabecular compartments. This automated method provides a consistent and robust tool for analyzing micro-CT scans of the trabecular bone in the mouse tibia, facilitating advancements in preclinical skeletal research.

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