Right ventricular strain and volume analyses through deep learning-based fully automatic segmentation based on radial long-axis reconstruction of short-axis cine magnetic resonance images.

Masateru KawakuboDaichi MoriyamaYuzo YamasakiKohtaro AbeKazuya HosokawaTetsuhiro MoriyamaPandji TriadyaksaAdi WibowoMichinobu NagaoHideo AraiHiroshi NishimuraToshiaki Kadokami

Published in: Magma (New York, N.Y.) (2022)

Our proposed RLA realizes accurate fully automatic extraction of the entire RV region from an available CMR cine image without any additional imaging. Our findings overcome the complexity of image analysis in CMR without the limitations of the RV visualization in echocardiography.

Keyphrases

deep learning
mycobacterium tuberculosis
magnetic resonance
convolutional neural network
high resolution
artificial intelligence
machine learning
left ventricular
computed tomography
pulmonary hypertension
heart failure
contrast enhanced
ultrasound guided
neural network