ArticleScientific reports2023
Automatic segmentation of trabecular and cortical compartments in HR-pQCT images using an embedding-predicting U-Net and morphological post-processing.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Review
- Micro-CT-based analysis of the murine osteochondral unit: a review and recommendations for standardized subchondral bone assessment.JBMR plus · 2026Review
- Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.Bone reports · 2026Review
- Longitudinal associations between changes in bone mechanical strength and fracture risk estimated by μFRAC.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026Article
- Localized bone deterioration predicts declining failure load and increased fracture risk.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026Article
- High-Resolution Peripheral Quantitative Computed Tomography (HR-pQCT) for Assessment of Avascular Necrosis of the Lunate.Journal of imaging · 2025Article
- Advancements in High-Resolution Computed Tomography: Revolutionising Bone Health Micro-Research.Bioengineering (Basel, Switzerland) · 2025Article
- A deep learning-based framework for standardized analysis of trabecular bone compartments from micro-CT imaging data in the mouse tibia.Scientific reports · 2025Article
- Quantifying localized bone loss in clinical computed tomography using void spaces.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- High-resolution peripheral quantitative computed tomography reliability and accuracy of bone density and morphology properties in children.JBMR plus · 2025Article
- A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone.Scientific reports · 2025Article
- Integrating deep learning and machine learning for improved CKD-related cortical bone assessment in HRpQCT images: A pilot study.Bone reports · 2025Article
- New approach to identifying elite winter sport athletes' risk of relative energy deficiency in sport (REDs).BMJ open sport & exercise medicine · 2025Article
- Three-Dimensional Bone-Image Synthesis with Generative Adversarial Networks.Journal of imaging · 2024Article
- Bone microarchitecture and strength assessed by HRpQCT in individuals with type 2 diabetes and prediabetes: the Maastricht study.JBMR plus · 2024Article
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Authors and funding
4 authors.
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Abstract
High-resolution peripheral quantitative computed tomography (HR-pQCT) is an emerging in vivo imaging modality for quantification of bone microarchitecture. However, extraction of quantitative microarchitectural parameters from HR-pQCT images requires an accurate segmentation of the image. The current standard protocol using semi-automated contouring for HR-pQCT image segmentation is laborious, introduces inter-operator biases into research data, and poses a barrier to streamlined clinical implementation. In this work, we propose and validate a fully automated algorithm for segmentation of HR-pQCT radius and tibia images. A multi-slice 2D U-Net produces initial segmentation predictions, which are post-processed via a sequence of traditional morphological image filters. The U-Net was trained on a large dataset containing 1822 images from 896 unique participants. Predicted segmentations were compared to reference segmentations on a disjoint dataset containing 386 images from 190 unique participants, and 156 pairs of repeated images were used to compare the precision of the novel and current protocols. The agreement of morphological parameters obtained using the predicted segmentation relative to the reference standard was excellent (R
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