RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity
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) sc...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-03253-9 |
| _version_ | 1797199689011953664 |
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| author | Jinwen Zhu Jieyun Bai Zihao Zhou Yaqi Liang Zhiting Chen Xiaoming Chen Xiaoshen Zhang |
| author_facet | Jinwen Zhu Jieyun Bai Zihao Zhou Yaqi Liang Zhiting Chen Xiaoming Chen Xiaoshen Zhang |
| author_sort | Jinwen Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T07:19:44Z |
| format | Article |
| id | doaj.art-a1307da65eab4451871d9831f7c1a006 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-04-24T07:19:44Z |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-a1307da65eab4451871d9831f7c1a0062024-04-21T11:08:15ZengNature PortfolioScientific Data2052-44632024-04-011111610.1038/s41597-024-03253-9RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial CavityJinwen Zhu0Jieyun Bai1Zihao Zhou2Yaqi Liang3Zhiting Chen4Xiaoming Chen5Xiaoshen Zhang6Department of Electronic Engineering, College of Information Science and Technology, Jinan UniversityDepartment of Electronic Engineering, College of Information Science and Technology, Jinan UniversityDepartment of Electronic Engineering, College of Information Science and Technology, Jinan UniversityDepartment of Electronic Engineering, College of Information Science and Technology, Jinan UniversityDepartment of Electronic Engineering, College of Information Science and Technology, Jinan UniversityDepartment of Cardiology, The First Affiliated Hospital of Jinan UniversityDepartment of Cardiology, The First Affiliated Hospital of Jinan UniversityAbstract 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.https://doi.org/10.1038/s41597-024-03253-9 |
| spellingShingle | Jinwen Zhu Jieyun Bai Zihao Zhou Yaqi Liang Zhiting Chen Xiaoming Chen Xiaoshen Zhang RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity Scientific Data |
| title | RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity |
| title_full | RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity |
| title_fullStr | RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity |
| title_full_unstemmed | RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity |
| title_short | RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity |
| title_sort | ras dataset a 3d cardiac lge mri dataset for segmentation of right atrial cavity |
| url | https://doi.org/10.1038/s41597-024-03253-9 |
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