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

作者：Amine Lagzouli, Lucinda Evans, Mark Hopkinson, Aikta Sharma, Natalia Mühl Castoldi, Davide Fontanarosa, Maria Antico, David M. L. Cooper, Alice Othmani, Vittorio Sansalone, Phil Salmon, Andrew A. Pitsillides, Peter Pivonka · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-19698-w · 被引用次数：1 · 研究领域：Bone health and osteoporosis research、Radiomics and Machine Learning in Medical Imaging、Bone and Joint Diseases

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...