Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network.

ABSTRACT:

Background

Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE has been hindered partly by nonstandardized image processing techniques, which can be operator and algorithm dependent. Minimal validation and limited access to transparent software platforms have also exacerbated the problem. This study aims to estimate atrial fibrosis from cardiac magnetic resonance scans using a reproducible operator-independent fully automatic open-source end-to-end pipeline.

Methods

A multilabel convolutional neural network was designed to accurately delineate atrial structures including the blood pool, pulmo

SUBMITTER: Razeghi O 

PROVIDER: S-EPMC7771635 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature