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AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Yuanfeng Ji · Haotian Bai · Chongjian GE · Jie Yang · Ye Zhu · Ruimao Zhang · Zhen Li · Lingyan Zhanng · Wanling Ma · Xiang Wan · Ping Luo

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #1031

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 https://amos22.grand-challenge.org.

Author Information

Yuanfeng Ji (University of Hong Kong)
Haotian Bai (Hong Kong University of Science and Technology)
Chongjian GE (The University of Hong Kong)
Jie Yang (The Chinese University of Hong Kong, Shenzhen)
Ye Zhu (The Chinese University of Hong Kong,Shenzhen)
Ruimao Zhang (The Chinese University of Hong Kong (Shenzhen))
Zhen Li (Chinese University of Hong Kong, Shenzhen)
Lingyan Zhanng
Wanling Ma
Xiang Wan (Shenzhen Research Institute of Big Data)
Ping Luo (The University of Hong Kong)

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