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.

Wolfgang WirthFelix EcksteinJana KemnitzChristian Frederik BaumgartnerEnder KonukogluDavid FuerstAkshay Sanjay Chaudhari

Published in: Magma (New York, N.Y.) (2020)

The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test-retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.

Keyphrases

deep learning
convolutional neural network
artificial intelligence
machine learning
magnetic resonance imaging
high throughput
cross sectional
rheumatoid arthritis
big data
contrast enhanced
high resolution
electronic health record
mass spectrometry
single cell
diffusion weighted imaging