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

Orod Razeghi,Iain Sim,Steven E Williams,Henry Chubb,Rashed Karim,Matthew Wright,John Whitaker,Caroline H Roney,Mark O’Neill,Louisa O’Neill,Rahul Mukherjee,Steven Niederer

doi:10.1161/circimaging.120.011512

Orod Razeghi, Iain Sim + Show 10 more

Open Access

https://doi.org/10.1161/circimaging.120.011512

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Abstract

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. A multilabel convolutional neural network was designed to accurately delineate atrial structures including the blood pool, pulmonary veins, and mitral valve. The output from the network removed the operator dependent steps in a reproducible pipeline and allowed for automated estimation of atrial fibrosis from LGE-cardiac magnetic resonance scans. The pipeline results were compared against manual fibrosis burdens, calculated using published thresholds: image intensity ratio 0.97, image intensity ratio 1.61, and mean blood pool signal +3.3 SD. We validated our methods on a large 3-dimensional LGE-cardiac magnetic resonance data set from 207 labeled scans. Automatic atrial segmentation achieved a 91% Dice score, compared with the mutual agreement of 85% in Dice seen in the interobserver analysis of operators. Intraclass correlation coefficients of the automatic pipeline with manually generated results were excellent and better than or equal to interobserver correlations for all 3 thresholds: 0.94 versus 0.88, 0.99 versus 0.99, 0.99 versus 0.96 for image intensity ratio 0.97, image intensity ratio 1.61, and +3.3 SD thresholds, respectively. Automatic analysis required 3 minutes per case on a standard workstation. The network and the analysis software are publicly available. Our pipeline provides a fully automatic estimation of fibrosis burden from LGE-cardiac magnetic resonance scans that is comparable to manual analysis. This removes one key source of variability in the measurement of atrial fibrosis.

Highlights

Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation
Our pipeline provides a fully automatic estimation of fibrosis burden from late gadolinium enhancement (LGE)-cardiac magnetic resonance scans that is comparable to manual analysis
We propose a novel end-to-end automatic pipeline for estimation of atrial fibrosis from LGECMR scans, including a convolutional neural network (CNN) for blood pool, pulmonary veins (PV), left atrial appendage (LAA), and mitral valve (MV) segmentation

Summary

Introduction

Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. 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 exacerbated the problem. This study aims to estimate atrial fibrosis from cardiac magnetic resonance scans using a reproducible operator-independent fully automatic opensource end-to-end pipeline

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