ArticleDiagnostics (Basel, Switzerland)2022
Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.Physiological reports · 2025Trial
- Subchondral bone abnormalities in the knee: imaging techniques, regional anatomy, and pathology.Skeletal radiology · 2026Review
- Automated knee MRI segmentation with KneeSeg-U for cartilage and bone extraction using an unsupervised deep learning framework.BMC musculoskeletal disorders · 2026Article
- Comparative evaluation of generative artificial intelligence models for synthetic knee radiograph augmentation in clinical research.BMC medical imaging · 2026Article
- Development of a multi-task deep learning system for classification of nine common knee abnormalities on MRI: a large-scale, multicentre, stepwise validation study.EClinicalMedicine · 2025Article
- A deep learning-based approach for measuring patellar cartilage deformations from knee MR images.Journal of biomechanics · 2025Article
- Towards MRI-Only Mandibular Resection Planning: CT-like Bone Segmentation from Routine T1 MRI Images Using Deep Learning.Craniomaxillofacial trauma & reconstruction · 2025Article
- Nanoparticle-enabled molecular imaging diagnosis of osteoarthritis.Materials today. Bio · 2025Review
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- A method framework of semi-automatic knee bone segmentation and reconstruction from computed tomography (CT) images.Quantitative imaging in medicine and surgery · 2024Article
- Measurement of Scapholunate Joint Space Width on Real-Time MRI-A Feasibility Study.Diagnostics (Basel, Switzerland) · 2024Article
- Combined Edge Loss UNet for Optimized Segmentation in Total Knee Arthroplasty Preoperative Planning.Bioengineering (Basel, Switzerland) · 2023Article
- Comparing bone shape models from deep learning processing of magnetic resonance imaging to computed tomography-based models.JSES international · 2023Article
- MR-Imaging in Osteoarthritis: Current Standard of Practice and Future Outlook.Diagnostics (Basel, Switzerland) · 2023Review
- Improving the Age Estimation Efficiency by Calculation of the Area Ratio Index Using Semi-Automatic Segmentation of Knee MRI Images.Biomedicines · 2023Article
- Auto-segmentation of the tibia and femur from knee MR images via deep learning and its application to cartilage strain and recovery.Journal of biomechanics · 2023Article
- Automatic segmentation of human knee anatomy by a convolutional neural network applying a 3D MRI protocol.BMC musculoskeletal disorders · 2023Article
- Atlas-Based Segmentation in Extraction of Knee Joint Bone Structures from CT and MR.Sensors (Basel, Switzerland) · 2022Article
- Automatic Bone Segmentation from MRI for Real-Time Knee Tracking in Fluoroscopic Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- Special Issue: "Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging".Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model's performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset.
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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.