Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry
出版年份 2022 全文链接
标题
Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and
MRI
Relaxometry
作者
关键词
-
出版物
JOURNAL OF MAGNETIC RESONANCE IMAGING
Volume -, Issue -, Pages -
出版商
Wiley
发表日期
2022-07-19
DOI
10.1002/jmri.28365
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Transfer learning for medical image classification: a literature review
- (2022) Hee E. Kim et al. BMC MEDICAL IMAGING
- fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data
- (2022) Ruiyang Zhao et al. Scientific Data
- Characterizing the transient response of knee cartilage to running: Decreases in cartilage T 2 of female recreational runners
- (2021) Hollis A Crowder et al. JOURNAL OF ORTHOPAEDIC RESEARCH
- Assessment of quantitative [18F]Sodium fluoride PET measures of knee subchondral bone perfusion and mineralization in osteoarthritic and healthy subjects
- (2021) L. Watkins et al. OSTEOARTHRITIS AND CARTILAGE
- Automatic knee cartilage and bone segmentation using multi-stage convolutional neural networks: data from the osteoarthritis initiative
- (2021) Anthony A. Gatti et al. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
- Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning
- (2021) Kevin A. Thomas et al. Cartilage
- Local MRI-based measures of thigh adipose tissue derived from fully automated deep convolutional neural network-based segmentation show a comparable responsiveness to bidirectional change in body weight as from quality controlled manual segmentation
- (2021) Jana Kemnitz et al. ANNALS OF ANATOMY-ANATOMISCHER ANZEIGER
- Prospective Deployment of Deep Learning in MRI : A Framework for Important Considerations, Challenges, and Recommendations for Best Practices
- (2020) Akshay S. Chaudhari et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort
- (2020) Wolfgang Wirth et al. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
- A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss – Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative
- (2020) Felix Eckstein et al. ARTHRITIS CARE & RESEARCH
- Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis
- (2019) Akshay S. Chaudhari et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Time-saving opportunities in knee osteoarthritis: T2 mapping and structural imaging of the knee using a single 5-min MRI scan
- (2019) Susanne M. Eijgenraam et al. EUROPEAN RADIOLOGY
- Epidemiology of osteoarthritis
- (2018) Ernest R. Vina et al. CURRENT OPINION IN RHEUMATOLOGY
- Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry
- (2018) Berk Norman et al. RADIOLOGY
- 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects
- (2018) Valentina Pedoia et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative
- (2018) Felix Ambellan et al. MEDICAL IMAGE ANALYSIS
- Five-minute knee MRI for simultaneous morphometry and T2 relaxometry of cartilage and meniscus and for semiquantitative radiological assessment using double-echo in steady-state at 3T
- (2017) Akshay S. Chaudhari et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- A simple analytic method for estimating T2 in the knee from DESS
- (2017) B. Sveinsson et al. MAGNETIC RESONANCE IMAGING
- Simultaneous bilateral-knee MR imaging
- (2017) Feliks Kogan et al. MAGNETIC RESONANCE IN MEDICINE
- Cluster analysis of quantitative MRI T 2 and T 1ρ relaxation times of cartilage identifies differences between healthy and ACL-injured individuals at 3T
- (2017) U.D. Monu et al. OSTEOARTHRITIS AND CARTILAGE
- Segmentation of joint and musculoskeletal tissue in the study of arthritis
- (2016) Valentina Pedoia et al. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
- Imaging research results from the Osteoarthritis Initiative (OAI): a review and lessons learned 10 years after start of enrolment
- (2014) Felix Eckstein et al. ANNALS OF THE RHEUMATIC DISEASES
- Quantitative measurement of femoral condyle cartilage in the knee by MRI: Validation study by multireaders
- (2013) Yasunari Fujinaga et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Quantitative MRI of articular cartilage and its clinical applications
- (2013) Xiaojuan Li et al. JOURNAL OF MAGNETIC RESONANCE IMAGING
- Osteoarthritis: A disease of the joint as an organ
- (2012) Richard F. Loeser et al. ARTHRITIS AND RHEUMATISM
- Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score)
- (2011) D.J. Hunter et al. OSTEOARTHRITIS AND CARTILAGE
- The diagnostic performance of MRI in osteoarthritis: a systematic review and meta-analysis
- (2011) L. Menashe et al. OSTEOARTHRITIS AND CARTILAGE
- Rapid estimation of cartilage T2 based on double echo at steady state (DESS) with 3 Tesla
- (2009) Goetz H. Welsch et al. MAGNETIC RESONANCE IN MEDICINE
- Intra- and inter-observer reproducibility of volume measurement of knee cartilage segmented from the OAI MR image set using a novel semi-automated segmentation method
- (2009) K.T. Bae et al. OSTEOARTHRITIS AND CARTILAGE
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