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 …
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 …
[HTML][HTML] Fully automatic knee joint segmentation and quantitative analysis for osteoarthritis from magnetic resonance (MR) images using a deep learning model
X Tang, D Guo, A Liu, D Wu, J Liu, N Xu… - Medical Science Monitor …, 2022 - ncbi.nlm.nih.gov
Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from
Magnetic Resonance (MR) Images Using a Deep Learning Model - PMC Back to Top Skip to …
Magnetic Resonance (MR) Images Using a Deep Learning Model - PMC Back to Top Skip to …
A multi-task deep learning method for detection of meniscal tears in MRI data from the osteoarthritis initiative database
We present a novel and computationally efficient method for the detection of meniscal tears
in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional …
in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional …
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 …
Meniscal lesion detection and characterization in adult knee MRI: a deep learning model approach with external validation
B Rizk, H Brat, P Zille, R Guillin, C Pouchy, C Adam… - Physica Medica, 2021 - Elsevier
Purpose Evaluation of a deep learning approach for the detection of meniscal tears and their
characterization (presence/absence of migrated meniscal fragment). Methods A large …
characterization (presence/absence of migrated meniscal fragment). Methods A large …
Deep learning reconstruction enables prospectively accelerated clinical knee MRI
Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep
learning (DL) methods have provided accelerated high-quality image reconstructions from …
learning (DL) methods have provided accelerated high-quality image reconstructions from …
[HTML][HTML] The optimisation of deep neural networks for segmenting multiple knee joint tissues from MRIs
DA Kessler, JW MacKay, VA Crowe… - … Medical Imaging and …, 2020 - Elsevier
Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster
and more reliable analysis of large datasets and to enable further downstream processing …
and more reliable analysis of large datasets and to enable further downstream processing …
[HTML][HTML] Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning
Background This study aimed to build a deep learning model to automatically segment
heterogeneous clinical MRI scans by optimizing a pre-trained model built from a …
heterogeneous clinical MRI scans by optimizing a pre-trained model built from a …
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 …