Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation
Yuanfeng Ji and Haotian Bai and Jie Yang and Ping Luo

@article{,
title= {Amos: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation},
year= {2022},
keywords= {computed tomography},
author= {Yuanfeng Ji and Haotian Bai and Jie Yang and others and Ping Luo},
abstract= {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. 


https://zenodo.org/record/7155725

**Reference:** Ji et al., AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation. NeurIPS 2022 Datasets and Benchmarks. https://arxiv.org/abs/2206.08023 (data: https://zenodo.org/records/7262581)},
terms= {},
license= {https://creativecommons.org/licenses/by/4.0/legalcode},
superseded= {},
url= {https://amos22.grand-challenge.org/}
}