Cuantificación del calcio aórtico en imágenes de tomografía usando redes neuronales convolucionales
Abstract
The detection of aortic calcifications with tomog raphy predicts cardiovascular diseases but their manual quantification is tedious. In this work, con volutional neural networks (CNNs) were trained to classify them automatically. The position of calcifications along with aortic geometry was an alyzed in 1415 patients. Axial, coronal and sagittal patches centered on each lesion candidate were digitally reconstructed. The candidate lesions con sisted of clusters of pixels with attenuation greater than 130HU around the aorta. The architecture of the CNNs was two convolutions and max-pooling blocks followed by two fully-connected layers. F1 score, accuracy, sensitivity and specificity were used as evaluation metrics. The axial network ob tained the best detection results in the ascending and arc portions while the sagittal network excelled in the descending portion, correctly detecting 95% of lesions
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