@inproceedings{5205b33b90ee41feb5951ba0acd153b4,
title = "SegBAW-Net: Segmentation of Bi-Atria and Wall Network Offering Valuable Insights into Challenge Data",
abstract = "Accurate segmentation of bi-atrial structures and their walls in patients with atrial fibrillation is essential for detailed anatomical analysis and patient-specific treatment planning. Determining the thickness of the atrial wall is particularly challenging due to significant regional variability and low image resolution, which existing approaches often overlook by applying a fixed thickness. Accurate segmentation of both atria and their walls can substantially improve outcome of catheter ablation by providing patient-specific treatment strategies. To address challenges in atrial segmentation, we developed and validated Segmentation of Bi-Atria and Wall Network (SegBAW-Net), a multistage deep neural network designed to automatically segment the left and right atria along with their walls. The performance of SegBAW-Net was evaluated using the Dice Similarity Coefficient and the 95\% Hausdorff Distance metrics. Data triaging was applied to ensure robust training and validation. The network was trained using 3D late gadolinium-enhanced magnetic resonance images provided by the MICCAI MBAS 2024 Challenge, which included 70 scans for training, 30 for validation, and 100 for testing. A key contribution of this paper is the in-depth analysis of both the data and ground truth by two experts.",
keywords = "Atrial fibrillation, Bi-Atria, Deep learning, Late gadolinium enhancement, Segmentation",
author = "Lefebvre, \{Arthur L.\} and Ishan Vatsaraj and Yamamoto, \{Carolyna A.P.\} and Kensuke Sakata and Brock Tice and Trayanova, \{Natalia A.\} and Kholmovski, \{Eugene G.\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; 15th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2024, Held in Conjunction with MICCAI 2024 ; Conference date: 10-10-2024 Through 10-10-2024",
year = "2025",
doi = "10.1007/978-3-031-87756-8\_34",
language = "English (US)",
isbn = "9783031877551",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "345--356",
editor = "Oscar Camara and Esther Puyol-Ant{\'o}n and Maxime Sermesant and Avan Suinesiaputra and Jichao Zhao and Chengyan Wang and Qian Tao and Alistair Young",
booktitle = "Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers",
address = "Germany",
}