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  1. Home
  3. Automatic Bi-atrial Segmentation And Biomarker Extraction From Late Gadolinium-enhanced Mri Using Deep Learning.
  1. Home
  3. Automatic Bi-atrial Segmentation And Biomarker Extraction From Late Gadolinium-enhanced Mri Using Deep Learning.

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Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning.

Fan Feng1, James Kennelly1, Zhaohan Xiong1

  • 1Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.

Expert Systems with Applications
|February 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new deep learning tool, biAtriaNet, accurately segments both atria and quantifies fibrosis, atrial wall thickness, and chamber volumes from LGE-MRIs. This advances personalized atrial fibrillation ablation strategies.

Keywords:
LGE-MRIatrial fibrillationcardiac magnetic resonance imagingdeep learningfibrosislate gadolinium-enhanced MRIsmedical image segmentation

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Biomedical Engineering

Background:

  • Atrial fibrillation (AF) involves progressive atrial remodeling, including dilation and fibrosis, impacting treatment efficacy.
  • Late gadolinium-enhanced (LGE) MRI quantifies left atrium (LA) fibrosis but lacks robust segmentation for both atria and accurate biomarker assessment.
  • Current methods often exclude the right atrium (RA) and struggle with precise anatomical and fibrotic characterization.

Purpose of the Study:

  • To introduce biAtriaNet, a deep learning pipeline for automated segmentation and biomarker extraction from LGE-MRIs of both LA and RA.
  • To evaluate atrial fibrosis, atrial wall thickness (AWT), and chamber dimensions/volumes for improved AF ablation guidance.
  • To develop a robust tool for patient-specific AF treatment strategies.

Main Methods:

  • Developed biAtriaNet, a deep learning pipeline using two CNNs with a modified U-Net architecture, residual connections, and batch normalization.
  • Trained and validated on 2D cine-MRIs (UK Biobank, n=4860) and 3D LGE-MRIs (University of Utah, n=60).
  • Independently tested on 11 3D LGE-MRIs (Waikato Hospital, New Zealand), comparing against expert annotations and ground truth.

Main Results:

  • biAtriaNet achieved high segmentation accuracy (Dice scores: LA 91.1%, RA 88.6%) and transferability to independent datasets.
  • Chamber volume and AWT measurements demonstrated high accuracy (>90% and 95.9% for LA, 94.6% for RA, respectively).
  • Fibrosis estimates showed strong correlations (Kolmogorov-Smirnov: LA 86.3%, RA 90.6%, p < 0.05).

Conclusions:

  • biAtriaNet enables accurate, automated, bi-atrial segmentation and biomarker extraction from LGE-MRIs.
  • The pipeline provides reliable quantification of atrial anatomy and fibrosis, crucial for AF management.
  • This tool holds significant potential for enhancing patient-specific AF ablation strategies and improving clinical outcomes.