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 AMOS website.
Dr. Zhen Li is currently an assistant professor at the School of Science and Engineering (SSE) of The Chinese University of Hong Kong (Shenzhen)/Future Intelligent Network Research Institute (FNii) of The Chinese University of Hong Kong (Shenzhen). He is also a research scientist at the Shenzhen Institute of Big Data (SRIBD) and a special researcher at the South China Hospital Affiliated to Shenzhen University. His research interests include interdisciplinary research in artificial intelligence, 3D vision, computer vision, and deep learning-assisted medical big data analysis. He has published more than 30 papers in top conferences and journals, such as top journals Cell Systems and Nature Communications, IEEE TNNLS, IEEE TMI, PLOS CB, etc. and top conferences CVPR, ICCV, ECCV, AAAI, IJCAI, ACL, ECAI, MICCAI , RECOMB, ISBI, etc. Dr. Li is one of the organizers for the AMOS 2022 Challenge.