Magnetic resonance image segmentation of the compressed spinal cord in patients with degenerative cervical myelopathy using convolutional neural networks.
Kyohei NozawaSatoshi MakiTakeo FuruyaSho OkimatsuTakaki InoueAtsushi YundeMasataka MiuraYuki ShirataniYasuhiro ShigaKazuhide InageYawara EguchiSeiji OhtoriSumihisa OritaPublished in: International journal of computer assisted radiology and surgery (2022)
Using deep learning with magnetic resonance images of deformed spinal cords as training data, we were able to segment compressed spinal cords of DCM patients with a high concordance with expert manual segmentation. In addition, the spinal cord CSA ratio was weakly, but significantly, correlated with neurological symptoms. Our study demonstrated the first steps needed to implement automated atlas-based analysis of DCM patients.
Keyphrases
- deep learning
- spinal cord
- convolutional neural network
- magnetic resonance
- artificial intelligence
- neuropathic pain
- spinal cord injury
- machine learning
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- big data
- prognostic factors
- contrast enhanced
- electronic health record
- virtual reality
- patient reported outcomes
- sleep quality
- cerebral ischemia
- data analysis