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

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, e

SUBMITTER: Lagzouli A 

PROVIDER: S-EPMC12521361 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature