Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views.

ABSTRACT:

Aims

Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views.

Methods and results

A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations (n = 1700, with 1400/100/200 cases for training/validating/testing). An independent test dataset from another institution was also evaluated, using cardiac magnetic resonance (CMR) 2CH and 4CH segmentations as input and 3D CCTA segmentations as the ground truth (n =

SUBMITTER: Xu H 

PROVIDER: S-EPMC10125223 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature