RAS Dataset

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.

当前有效治疗心房颤动（AF）的核心挑战，在于对人类心房复杂解剖结构的认知有限。对钆延迟增强磁共振成像（LGE-MRI）扫描中的右心房（RA）开展客观定量解读，高度依赖其精准分割。基于人工智能（AI）的右心房分割方法展现出良好的应用前景，但要实现该技术的落地，需依托大规模带标注的LGE-MRI图像开展模型训练。本文构建了一套完整的三维心脏数据集，包含50例高分辨率LGE-MRI扫描数据，每一例均经过细致的像素级标注。标注流程经由医学专家团队开展标准化众包审核，确保了标注结果的准确性与一致性。本数据集为该领域提供了极具价值的研究资源，可有力推动基于AI的右心房分割相关方法的发展，是该研究方向的一项重要贡献。

搜集汇总

背景与挑战

背景概述

RAS Dataset是一个包含50个高分辨率LGE-MRI扫描图像的3D心脏数据集，每个图像都经过像素级别的标注，用于支持基于AI的右心房分割研究。数据集经过医学专家的严格标准化，标注准确且一致，文件数量为52个，数据量为15.06 MB。