ArticleScientific reports2018
Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
73 citing papers in PubMed, 136 citations in OpenAlex.
- Automated 3D Analysis of Hip Morphology in a Young Adult Population.Annals of biomedical engineering · 2026Article
- Development of an automated landmarking tool for the femur in dual-energy X-ray absorptiometry scans using contour-based image analysis.JBMR plus · 2026Article
- Osteoporosis: current diagnostic possibilities, a literature review.Przeglad menopauzalny = Menopause review · 2026Review
- A new scheme for knee joint CT image segmentation: A max-flow and watershed method driving semi-automatic CT knee bone segmentation.Scientific reports · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Artificial Intelligence-Based Proximal Bone Shape Asymmetry Analysis and Clinical Correlation with Cartilage Relaxation Times and Functional Activity.Bioengineering (Basel, Switzerland) · 2026Article
- [Differentiation of osteoarthritis phenotypes on MRI using artificial intelligence].Orthopadie (Heidelberg, Germany) · 2026Review
- Knowledge mapping of osteoporotic fractures: a bibliometric analysis from 2014 to 2024.Archives of osteoporosis · 2025Review
- Magnetic Resonance Imaging-Based 3-Dimensional Models of the Pelvis and Hip Using Machine Learning for Automatic Bone Segmentation in a Dynamic Hip Impingement Simulation.Orthopaedic journal of sports medicine · 2025Article
- High-Fidelity Finite Element Modeling Technique to Improve Sensitivity to Bone Tissue Changes of Older Adults with Obesity undergoing Intensive Lifestyle Intervention.Annals of biomedical engineering · 2025Article
- Machine learning is changing osteoporosis detection: an integrative review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Review
- Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules.Journal of personalized medicine · 2025Review
- The application progress of artificial intelligence in osteoporosis diagnosis.Frontiers in artificial intelligence · 2025Review
- Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.Endocrine · 2024Article
- Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions.Medical physics · 2024Article
- Application of convolutional neural networks in medical images: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- The impact of data augmentation and transfer learning on the performance of deep learning models for the segmentation of the hip on 3D magnetic resonance images.Informatics in medicine unlocked · 2024Article
- Usefulness of T2-Weighted Images with Deep-Learning-Based Reconstruction in Nasal Cartilage.Diagnostics (Basel, Switzerland) · 2023Article
- Qualitative Classification of Proximal Femoral Bone Using Geometric Features and Texture Analysis in Collected MRI Images for Bone Density Evaluation.Sensors (Basel, Switzerland) · 2023Article
- A hybrid machine-learning approach for analysis of methane hydrate formation dynamics in porous media with synchrotron CT imaging.Journal of synchrotron radiation · 2023Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.