We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673 K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms—the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://www.zongweiz.com/dataset.

中文翻译：

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AbdomenAtlas：大规模、详细注释的多中心数据集，用于高效迁移学习和开放算法基准测试


我们引入了最大的腹部 CT 数据集（称为 AbdomenAtlas），其中包含 20,460 个三维 CT 体积，这些体积来自 112 家医院，涵盖不同的人群、地理位置和设施。 AbdomenAtlas 提供 673 K 高质量的腹部解剖结构掩模，由 10 名放射科医生组成的团队在 AI 算法的帮助下进行注释。我们首先让专家放射科医生手动注释 5,246 个 CT 体积中的 22 个解剖结构。接下来，对剩余的 CT 体积执行半自动注释程序，放射科医生修改 AI 预测的注释，反过来，AI 通过学习修改后的注释来改进其预测。需要如此大规模、详细注释和多中心的数据集有两个原因。首先，AbdomenAtlas 为大规模人工智能开发提供了重要资源，被称为大型预训练模型，可以减轻专家放射科医生的注释工作量，从而转移到更广泛的临床应用。其次，AbdomenAtlas为评估AI算法建立了大规模的基准——我们用于测试算法的数据越多，我们就越能保证在复杂的临床场景中可靠的性能。名为“BodyMaps：迈向人体 3D 图集”的 ISBI 和 MICCAI 挑战赛是使用 AbdomenAtlas 的子集发起的，旨在刺激 AI 创新并衡量分割准确性、推理效率和领域通用性。我们希望我们的 AbdomenAtlas 能够为更大规模的临床试验奠定基础，并为医学影像界的从业者提供特殊的机会。代码、模型和数据集可在 https://www.zongweiz.com/dataset 获取。