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Título : Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets
Autor : Balocco, Simone
González, Mauricio
Ñancule, Ricardo
Radeva, Petia
Thomas, Gabriel
Palabras clave : INTRAVASCULAR ULTRASOUND IMAGES;CONVOLUTIONAL NETS;DEEP LEARNING;MEDICAL IMAGE ANALYSIS
Fecha de publicación : 2018
Editorial : Springer
Citación : Balocco S., González M., Ñanculef R., Radeva P., Thomas G. (2018) Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets. In: Hernández Heredia Y., Milián Núñez V., Ruiz Shulcloper J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science, vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_4
Resumen : The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of F1-measure, precision and recall.
URI : https://repositorio.uci.cu/jspui/handle/123456789/9484
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