Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning.

ABSTRACT:

Aims

To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging.

Methods and results

One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice: 0.91, Hausdorff distance (HD): 6.18 mm] and LA (Dice: 0.93, HD:

SUBMITTER: Chen Z 

PROVIDER: S-EPMC8242184 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature