Coronary calcification identification in optical coherence tomography using convolutional neural networks

Dario A. B. Oliveira; Maysa M. G. Macedo; Pedro Nicz; Carlos Campos; Pedro Lemos; Marco A. Gutierrez

doi:10.1117/12.2293753

12 March 2018 Coronary calcification identification in optical coherence tomography using convolutional neural networks

Dario A. B. Oliveira, Maysa M. G. Macedo, Pedro Nicz, Carlos Campos, Pedro Lemos, Marco A. Gutierrez

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781Y (2018) https://doi.org/10.1117/12.2293753
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Intravascular optical coherence tomography (IOCT) is a modality that provides sufficient resolution for very accurate visualization of localized cardiovascular conditions, such as coronary artery calcification (CAC). CAC quantification in IOCT images is still performed mostly manually, which is time consuming, considering that each IOCT exam has more than two hundred 2D slices. An automated method for CAC detection in IOCT would add valuable information for clinicians when treating patients with coronary atherosclerosis. In this context, we propose an approach that uses a fully connected neural network (FCNN) for CAC detection in IOCT images using a small training dataset. In our approach, we transform the input image to polar coordinate transformation using as reference the centroid from the lumen segmentation, that restricts the variability in CAC spatial position, which we proved to be beneficial for the CNN training with few training data. We analyzed 51 slices from in-vivo human coronaries and the method achieved 63.6% sensitivity and 99.8% specificity for segmenting CAC. Our results demonstrate that it is possible to successfully detect and segment calcific plaques in IOCT images using FCNNs.

Citation Download Citation

Dario A. B. Oliveira, Maysa M. G. Macedo, Pedro Nicz, Carlos Campos, Pedro Lemos, and Marco A. Gutierrez "Coronary calcification identification in optical coherence tomography using convolutional neural networks", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781Y (12 March 2018); https://doi.org/10.1117/12.2293753

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available