Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.
Malte Maria SierenCornelia WidmannNick WeissJan Hendrik MoltzFlorian LinkFranz WegnerErik StahlbergMarco HornThekla Helene OechertingJan Peter GoltzJoerg BarkhausenAlex FrydrychowiczPublished in: European radiology (2021)
• A deep learning-based algorithm can automatically segment the aorta, mostly within acceptable margins of error, even if the vascular architecture is altered by disease. • Quantifications performed in the segmentations were mostly within clinically acceptable limits, even in pathologically altered segments of the aorta.
Keyphrases
- deep learning
- aortic valve
- pulmonary artery
- convolutional neural network
- artificial intelligence
- machine learning
- computed tomography
- coronary artery
- pulmonary hypertension
- aortic dissection
- pulmonary arterial hypertension
- contrast enhanced
- image quality
- dual energy
- magnetic resonance imaging
- magnetic resonance
- positron emission tomography
- high throughput