CNN-based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures.
Nikita VladimirovEkaterina BruiAnatoliy LevchukWalid Al-HaidriVladimir FokinAleksandr EfimtcevDavid BendahanPublished in: Magnetic resonance in medicine (2023)
U-Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine-tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non-MRI method.
Keyphrases
- convolutional neural network
- deep learning
- end stage renal disease
- extracellular matrix
- newly diagnosed
- chronic kidney disease
- contrast enhanced
- ejection fraction
- machine learning
- magnetic resonance imaging
- peritoneal dialysis
- air pollution
- magnetic resonance
- working memory
- patient reported outcomes
- high intensity
- neural network