Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approach.

ABSTRACT:

Objectives

To develop and evaluate a multi-task deep-learning (DL) model for automated segment-level coronary artery calcium (CAC) scoring on non-contrast computed tomography (CT) for precise localization and quantification of calcifications in the coronary artery tree.

Methods

This study included 1514 patients (mean age, 60.0 ± 10.2 years; 56.0% female) with stable chest pain from 26 centers participating in the multicenter DISCHARGE trial (NCT02400229). The patients were randomly assigned to a training/validation set (1059) and a test set (455). We developed a multi-task neural network for performing the segmentation of calcifications on the segment level as the main task and the segmentation of coronary artery segment regions with weak annotations as an auxiliary task. Mo

SUBMITTER: Follmer B 

PROVIDER: S-EPMC11484984 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature