Knee menisci segmentation and relaxometry of 3D ultrashort echo time (UTE) cones MR imaging using attention U-Net with transfer learning

no code implementations • 5 Aug 2019 • Michal Byra, Mei Wu, Xiaodong Zhang, Hyungseok Jang, Ya-Jun Ma, Eric Y Chang, Sameer Shah, Jiang Du

Next, the T1, T1$\rho$, T2* relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. The models developed using ROIs provided by two radiologists achieved high Dice scores of 0. 860 and 0. 833, while the radiologists' manual segmentations achieved a Dice score of 0. 820.

Segmentation Transfer Learning