Scientific Data (Apr 2024)

RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity

  • Jinwen Zhu,
  • Jieyun Bai,
  • Zihao Zhou,
  • Yaqi Liang,
  • Zhiting Chen,
  • Xiaoming Chen,
  • Xiaoshen Zhang

DOI
https://doi.org/10.1038/s41597-024-03253-9
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 6

Abstract

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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.