ArticleMagnetic resonance in medicine2018
Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.
Article in Magnetic resonance in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 2 of them syntheses that pooled it.
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Who cites it
132 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.Academic radiology · 2019Pooled it
- Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability.PloS one · 2018Pooled it
- Comparison of Deep-Learning-Based Reconstruction of Knee MRI with Conventional Sequences in Patients with Knee Pain.Diagnostics (Basel, Switzerland) · 2026Article
- Longitudinal 3D quantification of joint space from CT images: Semi-automatic segmentation and feasibility of nnU-Net automation.Osteoarthritis imaging · 2026Article
- A generative adversarial network incorporating dual attention mechanisms for the segmentation of knee bone and cartilage.Magma (New York, N.Y.) · 2026Article
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- Diagnosis of SLAP lesions on shoulder MRI using a 2.5D deep learning and ensemble learning framework.Frontiers in surgery · 2026Article
- 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
- Clinical Applications, Challenges & Pitfalls, and Recommendations for Large Language Model and Generative AI in Musculoskeletal Imaging.Journal of the Korean Society of Radiology · 2025Review
- A fully automatic knee subregion segmentation network based on tissue segmentation and anatomical geometry.Scientific reports · 2025Article
- Artificial intelligence-assisted analysis of musculoskeletal imaging-A narrative review of the current state of machine learning models.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Review
- Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluation of an automated laminar cartilage T2 relaxation time analysis method in an early osteoarthritis model.Skeletal radiology · 2025Observational
- Estimation of Trabecular Bone Volume with Dual-Echo Ultrashort Echo Time (UTE) Magnetic Resonance Imaging (MRI) Significantly Correlates with High-Resolution Computed Tomography (CT).Journal of imaging · 2025Article
- Multi-class segmentation of knee MRI based on hybrid attention.Frontiers in medicine · 2025Article
- An improved YOLOv10-based framework for knee MRI lesion detection with enhanced small object recognition and low contrast feature extraction.Frontiers in artificial intelligence · 2025Article
- Automatic knee osteoarthritis severity grading based on X-ray images using a hierarchical classification method.Arthritis research & therapy · 2024Article
- A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.Quantitative imaging in medicine and surgery · 2024Article
- Deep Convolutional Neural Network for Dedicated Regions-of-Interest Based Multi-Parameter Quantitative Ultrashort Echo Time (UTE) Magnetic Resonance Imaging of the Knee Joint.Journal of imaging informatics in medicine · 2024Article
- Can generative artificial intelligence pass the orthopaedic board examination?Journal of orthopaedics · 2024Article
72 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
purposeTo describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint.
methodsA fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts.
resultsThe proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions.
conclusionThe study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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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.