Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and …
B Norman, V Pedoia, S Majumdar - Radiology, 2018 - pubs.rsna.org
Purpose To analyze how automatic segmentation translates in accuracy and precision to
morphology and relaxometry compared with manual segmentation and increases the speed …
morphology and relaxometry compared with manual segmentation and increases the speed …
Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning
Purpose To develop a deep learning‐based method for knee menisci segmentation in 3D
ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation …
ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation …
Deep learning‐based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative
Morphological changes in knee cartilage subregions are valuable imaging‐based
biomarkers for understanding progression of osteoarthritis, and they are typically detected …
biomarkers for understanding progression of osteoarthritis, and they are typically detected …
Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging
Purpose To describe and evaluate a new fully automated musculoskeletal tissue
segmentation method using deep convolutional neural network (CNN) and three …
segmentation method using deep convolutional neural network (CNN) and three …
Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks
Purpose Fully automatic tissue segmentation is an essential step to translate quantitative
MRI techniques to clinical setting. The goal of this study was to develop a novel approach …
MRI techniques to clinical setting. The goal of this study was to develop a novel approach …
Automatic segmentation of high-and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative
Clinical studies including thousands of magnetic resonance imaging (MRI) scans offer
potential for pathogenesis research in osteoarthritis. However, comprehensive quantification …
potential for pathogenesis research in osteoarthritis. However, comprehensive quantification …
3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior …
V Pedoia, B Norman, SN Mehany… - Journal of Magnetic …, 2019 - Wiley Online Library
Background Semiquantitative assessment of MRI plays a central role in musculoskeletal
research; however, in the clinical setting MRI reports often tend to be subjective and …
research; however, in the clinical setting MRI reports often tend to be subjective and …
The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset
Purpose To organize a multi-institute knee MRI segmentation challenge for characterizing
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …
the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring …
Deep convolutional neural network for segmentation of knee joint anatomy
Purpose To describe and evaluate a new segmentation method using deep convolutional
neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex …
neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex …
Automatic knee cartilage segmentation using fully volumetric convolutional neural networks for evaluation of osteoarthritis
Automated Cartilage segmentation is essential for improving the performance of advanced
Knee Osteoarthritis (OA) assessment due to its convoluted 3D structure. In this paper, we …
Knee Osteoarthritis (OA) assessment due to its convoluted 3D structure. In this paper, we …