Key result
The MBAS 2024 challenge provides datasets and source code from 13 participating teams for the automatic semantic segmentation of bi-atrial anatomical structures using 3D LGE-MRI.
Why the study?
Accurate automatic semantic segmentation of bi-atrial anatomical structures using 3D LGE-MRI is a crucial preliminary step for clinical evaluation and patient-specific ablation treatments for AF.
This repository provides the dataset and deep learning source code for the MBAS 2024 challenge on automatic semantic segmentation of bi-atrial structures from 3D LGE-MRI.
Should not yet change AF imaging protocols; leaves open automated segmentation impact on ablation or HF outcomes.
Overview This repository contains the dataset splits, pre-processed data, source code, and model checkpoints for our solution to the MBAS 2024 (Multi-class Bi-Atrial Segmentation) Challenge, held in conjunction with MICCAI 2024. The MBAS 2024 challenge focuses on the automatic semantic segmentation of bi-atrial anatomical structures (both the left and right atria, along with their walls) using 3D Late Gadolinium-Enhanced Magnetic Resonance Imaging (LGE-MRI). Accurate segmentation of these structures is a crucial preliminary step for clinical evaluation and patient-specific ablation treatments for Atrial Fibrillation (AF). Contents of this Repository Training Data: Training data consist of raw labels mapped to five semantic classes: 0—background (BAK), 1—right atrium (RA), 2—left atrium (LA), 3—right atrial wall (RAW), and 4—left atrial wall (LAW). Test Data: TBD. Framework Source Code: Complete deep learning pipelines detailing our data preprocessing, model training, cross-validation, and inference procedures. (Coming soon on GitHub) Team Source Code: Source code submitted by 13 participating teams (please note that model weights are not included). Links & References Official MBAS 2024 Challenge Page: MBAS24 Conference: MICCAI & STACOM 2024 Associated Paper/Report: TBD Acknowledgments We sincerely thank the multiple institutions that generously provided the data for this challenge. We also extend our deepest gratitude to all of our team members for their hard work, dedication, and support throughout the competition.
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Xu et al. (2026) studied Atrial Fibrillation (AF). Automatic semantic segmentation was evaluated. The MBAS 2024 challenge provides datasets and source code from 13 participating teams for the automatic semantic segmentation of bi-atrial anatomical structures using 3D LGE-MRI.
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