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https://observatorio.fm.usp.br/handle/OPI/37572
Title: | Using Convolutional Neural Networks for Classification of Bifurcation Regions in IVOCT Images |
Authors: | MIYAGAWA, M.; COSTA, M. G. F.; GUTIERREZ, M. A.; COSTA, J. P. G. F.; COSTA FILHO, C. F. F. |
Citation: | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), p.5597-5600, 2019 |
Abstract: | Optical Coherence Tomography (OCT) technology enabled the experts to analyze coronary lesions from high-resolution intravascular images. Studies have shown the relationship between bifurcation regions and a higher occurrence of wall thickening and lesions in these areas. Some level of automation could benefit experts, since examining pullback frames is a laborious and time-consuming task. Although Convolutional Neural Networks (CNN) have shown promising results in classification tasks of medical images, we did not identify the use of CNN's in IVOCT images to classify bifurcation regions in the literature. In this work, we evaluated a CNN architecture in the bifurcation classification task trained with IVOCT images from 9 pullbacks from 9 different patients. We used data augmentation to balance the dataset, due to the low amount of bifurcation-labeled frames. Our classification results are comparable to other works in the literature, presenting better result in AUC (99.70%). |
Appears in Collections: | Comunicações em Eventos - HC/InCor Comunicações em Eventos - HC/InRad Comunicações em Eventos - LIM/65 |
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art_MIYAGAWA_Using_Convolutional_Neural_Networks_for_Classification_of_Bifurcation_2019.PDF Restricted Access | publishedVersion (English) | 817.08 kB | Adobe PDF | View/Open Request a copy |
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