High-quality chest CT segmentation to assess the impact of COVID-19 disease.
Michele BertoliniAlma BrambillaSamanta DallastaGiorgio ColomboPublished in: International journal of computer assisted radiology and surgery (2021)
Automatic algorithms allowed for a substantial reduction in segmentation time. However, a great effort was required for the manual identification of COVID-19 CT manifestations. The developed automated procedure succeeded in obtaining sufficiently accurate models of the airways and the lungs of both healthy patients and subjects with confirmed COVID-19, in a reasonable time.
Keyphrases
- deep learning
- coronavirus disease
- sars cov
- machine learning
- end stage renal disease
- convolutional neural network
- computed tomography
- image quality
- newly diagnosed
- dual energy
- chronic kidney disease
- ejection fraction
- contrast enhanced
- respiratory syndrome coronavirus
- peritoneal dialysis
- cystic fibrosis
- positron emission tomography
- magnetic resonance imaging
- high resolution
- high throughput
- magnetic resonance
- minimally invasive
- mass spectrometry