Coronary calcification segmentation in intravascular OCT images using deep learning: application to calcification scoring.

ABSTRACT: Major calcifications are of great concern when performing percutaneous coronary interventions because they inhibit proper stent deployment. We created a comprehensive software to segment calcifications in intravascular optical coherence tomography (IVOCT) images and to calculate their impact using the stent-deployment calcification score, as reported by Fujino et al. We segmented the vascular lumen and calcifications using the pretrained SegNet, convolutional neural network, which was refined for our task. We cleaned segmentation results using conditional random field processing. We evaluated the method on manually annotated IVOCT volumes of interest (VOIs) without lesions and with calcifications, lipidous, or mixed lesions. The dataset included 48 VOIs taken from 34 clinical pullbacks, gi

SUBMITTER: Gharaibeh Y 

PROVIDER: S-EPMC6934132 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature