2022
DOI: 10.3389/fcvm.2022.989091
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Automatic view classification of contrast and non-contrast echocardiography

Abstract: BackgroundContrast and non-contrast echocardiography are crucial for cardiovascular diagnoses and treatments. Correct view classification is a foundational step for the analysis of cardiac structure and function. View classification from all sequences of a patient is laborious and depends heavily on the sonographer’s experience. In addition, the intra-view variability and the inter-view similarity increase the difficulty in identifying critical views in contrast and non-contrast echocardiography. This study ai… Show more

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Cited by 3 publications
(1 citation statement)
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“…All datasets were anonymized and sensitive information around the images was removed. This study employed an independently developed convolutional neural network to classify 2DE and LVO A4C videos 18 automatically. Due to the differences in image sizes captured by different equipment, all videos were resized to 256 × 256 pixels by linear interpolation, and the intensity was normalized to [–1, 1].…”
Section: Methodsmentioning
confidence: 99%
“…All datasets were anonymized and sensitive information around the images was removed. This study employed an independently developed convolutional neural network to classify 2DE and LVO A4C videos 18 automatically. Due to the differences in image sizes captured by different equipment, all videos were resized to 256 × 256 pixels by linear interpolation, and the intensity was normalized to [–1, 1].…”
Section: Methodsmentioning
confidence: 99%