Abdominal multi-organ segmentation is one of the most attractive topics in the field of medical image analysis, which plays an important role in supporting clinical workflows such as disease diagnosis and treatment planning. The recent success of deep learning methods applied for abdominal multi-organ segmentation expose the lack of large-scale comprehensive benchmarks for developing and comparing such methods. While several benchmark datasets [1-4] for abdominal organ segmentation are available, the limited number of organs of interest and training samples still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of different methods. To address the above drawbacks and further promote the development of medical image segmentation technology, we present AMOS, a large-scale, clinical and diverse abdominal multi-organ segmentation benchmark. It 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. It is the most comprehensive benchmark of its kind to date. Specifically, the AMOS 2022 challenge contains two tasks in which the participating teams can take place and submit their result(s): a) Task 1 - Segmentation of abdominal organs (CT only): as a mostly regular task, Task 1 aims to comprehensively evaluate the performance of different segmentation methods across large-scale and great diversity CT scans, a total of 500 cases with annotations of 15 organs (spleen, right kidney, left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, right adrenal gland, left adrenal gland, duodenum, bladder, prostate/uterus) are presented. b) Task 2 - Segmentation of abdominal organs (CT & MRI): this task extends the image modality target of Task 1 to the MRI modality. Under such a “Cross Modality” setting, a single algorithm is required to segment abdominal organs from both CT and MRI. Specifically, additional 100 MRI scans with the same type of annotation will be provided. Totally, the AMOS 2022 challenge focuses on the comprehensive evaluation of state-of-the-art methods for the segmentation of abdominal multi-organ in both clinical CT and MRI scans. With this challenge, we hope to bring together researchers and practitioners at the interplay of medical image analysis, computer vision, and machine learning to contribute their effort to the development of the corresponding techniques. We will also provide workshops to encourage participants to discuss open problems in the related areas. [1] Bilic, P., Christ, P. F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., ... & Menze, B. H. (2019). The liver tumor segmentation benchmark (lits). arXiv preprint arXiv:1901.04056. [2] Heller, N., Isensee, F., Maier-Hein, K. H., Hou, X., Xie, C., Li, F., ... & Weight, C. (2021). The state of the art in kidney and kidney tumor segmentation in contrast-enhanced ct imaging: Results of the kits19 challenge. Medical Image Analysis, 67, 101821. [3] Kavur, A. E., Gezer, N. S., Bar, M., Aslan, S., Conze, P. H., Groza, V., ... & Selver, M. A. (2021). CJAOS challengecombined (CT-MR) healthy abdominal organ segmentation. Medical Image Analysis, 69, 101950. [4] Ma, J. (2021). Flare21 - Grand Challenge. grand. Retrieved December 2, 2021, from https ://flare.grandchallenge. org/FLARE21/.