ReviewMedical & biological engineering & computing2022
A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images.
Review in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Segmentation methods for quantifying X-ray Computed Tomography based biomarkers to assess hip fracture risk: a systematic literature review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- Deep Learning for Analysis of Bone Marrow Adiposity: Breakthroughs from Recent Large-Scale Analyses in the UK Biobank.Current osteoporosis reports · 2026Review
- Automated neural network femur segmentation performance in computed tomography images of older adults with obesity.JBMR plus · 2026Article
- DIKOApp: An AI-Based Diagnostic System for Knee Osteoarthritis.Journal of imaging informatics in medicine · 2025Article
- A Two stage deep learning network for automated femoral segmentation in bilateral lower limb CT scans.Scientific reports · 2025Article
- 3D-printed hip prostheses with regenerative integration: a state-of-the-art comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions.Medical physics · 2024Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH.Biomedical engineering online · 2024Article
- Clinical validation of artificial intelligence-based preoperative virtual reduction for Neer 3- or 4-part proximal humerus fractures.BMC musculoskeletal disorders · 2024Article
- Finite element models with automatic computed tomography bone segmentation for failure load computation.Scientific reports · 2024Article
- SSDL-an automated semi-supervised deep learning approach for patient-specific 3D reconstruction of proximal femur from QCT images.Medical & biological engineering & computing · 2024Article
- Development and validation of a semi-automated and unsupervised method for femur segmentation from CT.Scientific reports · 2024Article
- Automatization of CT Annotation: Combining AI Efficiency with Expert Precision.Diagnostics (Basel, Switzerland) · 2024Article
- Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.Scientific reports · 2023Article
- Improved distinct bone segmentation in upper-body CT through multi-resolution networks.International journal of computer assisted radiology and surgery · 2023Article
- Article
- Integration of Square Fiducial Markers in Patient-Specific Instrumentation and Their Applicability in Knee Surgery.Journal of personalized medicine · 2023Article
- Computer-assisted preoperative planning of bone fracture fixation surgery: A state-of-the-art review.Frontiers in bioengineering and biotechnology · 2022Review
- Application of Methods for a Morphological Analysis of the Femoral Diaphysis Based on Clinical CT Images to Prehistoric Human Bone: Comparison of Modern Japanese and Jomon Populations from Hegi Cave, Oita, Japan.BioMed research international · 2022Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
Automatic CT segmentation of proximal femur has a great potential for use in orthopedic diseases, especially in the imaging-based assessments of hip fracture risk. In this study, we proposed an approach based on deep learning for the fast and automatic extraction of the periosteal and endosteal contours of proximal femur in order to differentiate cortical and trabecular bone compartments. A three-dimensional (3D) end-to-end fully convolutional neural network (CNN), which can better combine the information among neighbor slices and get more accurate segmentation results by 3D CNN, was developed for our segmentation task. The separation of cortical and trabecular bones derived from the QCT software MIAF-Femur was used as the segmentation reference. Two models with the same network structures were trained, and they achieved a dice similarity coefficient (DSC) of 97.82% and 96.53% for the periosteal and endosteal contours, respectively. Compared with MIAF-Femur, it takes half an hour to segment a case, and our CNN model takes a few minutes. To verify the excellent performance of our model for proximal femoral segmentation, we measured the volumes of different parts of the proximal femur and compared it with the ground truth, and the relative errors of femur volume between predicted result and ground truth are all less than 5%. This approach will be expected helpful to measure the bone mineral densities of cortical and trabecular bones, and to evaluate the bone strength based on FEA.
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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.