Automatic pulmonary vessel segmentation on noncontrast chest CT: deep learning algorithm developed using spatiotemporally matched virtual noncontrast images and low-keV contrast-enhanced vessel maps.
Ju Gang NamJoseph Nathanael WitantoSang Joon ParkSeung Jin YooJin Mo GooSoon-Ho YoonPublished in: European radiology (2021)
• We developed a deep learning pulmonary vessel segmentation algorithm using virtual noncontrast images and 50-keV enhanced images produced by a dual-source CT scanner. • Our algorithm successfully segmented vessels on diseased lungs. • Our algorithm showed promising results in assessing the loss of small vessel density in COPD patients.
Keyphrases
- deep learning
- dual energy
- contrast enhanced
- computed tomography
- convolutional neural network
- image quality
- artificial intelligence
- machine learning
- diffusion weighted
- magnetic resonance imaging
- pulmonary hypertension
- end stage renal disease
- magnetic resonance
- chronic kidney disease
- ejection fraction
- chronic obstructive pulmonary disease
- diffusion weighted imaging
- lung function
- peritoneal dialysis
- prognostic factors
- cystic fibrosis
- air pollution
- pet ct
- neural network