@InProceedings{JoseMarone2016,
author="Jose Marone
and Simone Balocco
and Marc Bola{\~n}os
and Jose Massa
and Petia Radeva",
title="Learning the Lumen Border using a Convolutional Neural Networks classifier",
booktitle="19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop",
year="2016",
abstract="IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. Tosolve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93\% and F-score of 71\% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows.",
optnote="MILAB;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2822), last updated on Fri, 26 Feb 2021 14:22:30 +0100",
file=":http://refbase.cvc.uab.es/files/MBB2016.pdf:PDF"
}