External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.
Krit DwivediMichael SharkeySamer AlabedCurtis P LanglotzAndy J SwiftChristian BluethgenPublished in: European radiology (2023)
• Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.
Keyphrases
- deep learning
- contrast enhanced
- pulmonary hypertension
- convolutional neural network
- computed tomography
- artificial intelligence
- interstitial lung disease
- magnetic resonance imaging
- diffusion weighted
- magnetic resonance
- dual energy
- systemic sclerosis
- machine learning
- pulmonary artery
- diffusion weighted imaging
- optical coherence tomography
- positron emission tomography
- idiopathic pulmonary fibrosis
- mass spectrometry
- coronary artery