TY - GEN
T1 - Uncertainty measurement and confidence calibration for calcium detection in optical coherence images
AU - Liu, Hongshan
AU - Bamba, Abdul Latif
AU - Gan, Yu
N1 - Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Calibration
KW - Coronary Artery Disease
KW - Deep Learning
KW - Optical Coherence Tomography
UR - http://www.scopus.com/inward/record.url?scp=85159656827&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85159656827&partnerID=8YFLogxK
U2 - 10.1117/12.2652944
DO - 10.1117/12.2652944
M3 - Conference contribution
AN - SCOPUS:85159656827
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII
A2 - Izatt, Joseph A.
A2 - Fujimoto, James G.
T2 - Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVII 2023
Y2 - 30 January 2023 through 1 February 2023
ER -