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. Information can be found at this https URL

介绍了 AMOS 数据集，用于细分包含多个器官的腹部 CT/MRI 图像，并对该数据集上使用的多种深度学习模型进行了基准测试。