Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation

Despite the considerable progress in automatic abdominal multi-organ segmentation from CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is hampered by the lack of a large-scale benchmark from diverse clinical scenarios. Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods. To mitigate the limitations, we present AMOS, a large-scale, diverse, clinical dataset for abdominal organ segmentation. AMOS provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs, providing challenging examples and test-bed for studying robust segmentation algorithms under diverse targets and scenarios. We further benchmark several state-of-the-art medical segmentation models to evaluate the status of the existing methods on this new challenging dataset. We have made our datasets, benchmark servers, and baselines publicly available, and hope to inspire future research.

尽管近年来基于计算机断层扫描（CT）与磁共振成像（MRI）扫描的腹部多器官自动分割技术已取得显著进展，但由于缺乏覆盖多样临床场景的大规模基准数据集，对模型性能开展全面评估仍存在诸多阻碍。受限于三维医学数据的收集与标注成本高昂，目前绝大多数深度学习模型均由仅包含有限目标器官或样本量的数据集驱动，这一现状仍限制了现代深度学习模型的性能潜力，也难以对各类分割方法开展全面且公允的性能评估。为缓解上述研究局限，我们构建了AMOS——一款面向腹部器官分割任务的大规模、多样化临床基准数据集。AMOS包含500例CT与100例MRI扫描数据，其采集自多中心、多设备厂商、多模态、多扫描时相且涵盖多种疾病的患者群体，每例数据均带有15个腹部器官的体素级标注，可为在多样化目标与场景下研究鲁棒性分割算法提供极具挑战性的测试样本与实验平台。我们进一步对多款当前顶尖的医学分割模型开展基准测试，以评估现有方法在这款全新挑战性数据集上的性能表现。目前我们已将该数据集、基准测试服务器与基线模型公开共享，以期为后续相关研究提供启发与助力。