Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning
Magnetic Resonance in Medicine2019Vol. 83(3), pp. 1109–1122
Citations Over TimeTop 10% of 2019 papers
Michał Byra, Mei Wu, Xiaodong Zhang, Hyungseok Jang, Yajun Ma, Eric Y. Chang, Sameer B. Shah, Jiang Du
Abstract
The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.
Related Papers
- → Inferring brain tissue composition and microstructure via MR relaxometry(2018)142 cited
- → Proton Nuclear Magnetic Resonance (NMR) Relaxometry in Soil Science Applications(2010)76 cited
- → Fast multicomponent 3D‐T1ρ relaxometry(2020)9 cited
- → A Look at NMR Relaxometry Applications in Meat Science – RecentAdvances in Coupling NMR Relaxometry with Spectroscopic, Thermodynamic,Microscopic and Sensory Measurements(2009)10 cited
- → Relaxometry: Applications in the Body(2020)