Article ; Online: RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity.
2024 Volume 11, Issue 1, Page(s) 401
Abstract: 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 ... ...
Abstract | 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) 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 RA segmentation methods. |
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MeSH term(s) | Humans ; Artificial Intelligence ; Atrial Fibrillation/pathology ; Gadolinium ; Heart Atria/diagnostic imaging ; Heart Atria/pathology ; Magnetic Resonance Imaging/methods |
Chemical Substances | Gadolinium (AU0V1LM3JT) |
Language | English |
Publishing date | 2024-04-20 |
Publishing country | England |
Document type | Dataset ; Journal Article |
ZDB-ID | 2775191-0 |
ISSN | 2052-4463 ; 2052-4463 |
ISSN (online) | 2052-4463 |
ISSN | 2052-4463 |
DOI | 10.1038/s41597-024-03253-9 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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