SignificanceOptical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). However, unidentified calcified regions within a narrowed artery could impair the outcome of the treatment. Fast and objective identification is paramount to automatically procuring accurate readings on calcifications within the artery.AimWe aim to rapidly identify calcification in coronary OCT images using a bounding box and reduce the prediction bias in automated prediction models.ApproachWe first adopt a deep learning-based object detection model to rapidly draw the calcified region from coronary OCT images using a bounding box. We measure the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result’s confidence and center coordinates.ResultsWe implemented an object detection module to draw the boundary of the calcified region at a rate of 140 frames per second. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection and eliminate the estimation bias from various object detection methods. The calibrated confidence of prediction results in a confidence error of ∼0.13, suggesting that the confidence calibration on calcification detection could provide a more trustworthy result.ConclusionsGiven the rapid detection and effective calibration of the proposed work, we expect that it can assist in clinical evaluation of treating the CAD during the imaging-guided procedure.
Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). Image-guided solutions such as Percutaneous Coronary Intervention (PCI) are extensively used during the treatment of CAD. However, unidentified calcified regions within a narrowed artery could impair the outcome of the PCI. Prior to treatments, object detection of the diseased regions is paramount to automatically procure accurate readings on calcifications within the artery. Though deep learning-based object detection methods have been explored in a variety of applications, the quality of predictions can be negatively impacted by overconfident deep learning models, which is not desirable in safety-critical scenarios. In this work, we adopt an object detection model to rapidly draw the calcified region from coronary OCT images using bounding box. We evaluate the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. The calibrated confidence of prediction results in a confidence error of approximately 0.13, suggesting that the confidence calibration on calcification detection could provide a more trustworthy result, which indicates a great potential to assist clinical evaluation of treating the CAD during the imaging-guided procedure.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.