2026-10-14T16:29:49Z https://www.scidb.cn/oai

10.57760/sciencedb.16572 2024-03-28T20:17:28Z

2024-03-28 RAS Dataset doi:10.57760/sciencedb.16572 en The current challenge in effectively treating atrial fibrillation (AF) stems from a limited understanding of the intricate structure of the human atria. The objective and quantitative interpretation of the right atrium (RA) in late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) scans relies heavily on its precise segmentation. Leveraging the potential of artificial intelligence (AI) - based approaches for RA segmentation presents a promising solution. However, the successful implementation of AI in this context necessitates access to a substantial volume of annotated LGE-MRI images for model training. In this paper, we present a comprehensive 3D cardiac dataset comprising 50 high-resolution LGE-MRI scans, each meticulously annotated at the pixel level. The annotation process underwent rigorous standardization through crowdsourcing among a panel of medical experts, ensuring the accuracy and consistency of the annotations. Our dataset represents a significant contribution to the field, providing a valuable resource for advancing AI-based RA segmentation methods. atrial fibrillation; AI; MRI; Right atrium Bai Jieyun PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank