Performing coarse-to-fine abdominal multi-organ segmentation facilitates to\nextract high-resolution segmentation minimizing the lost of spatial contextual\ninformation. However, current coarse-to-refine approaches require a significant\nnumber of models to perform single organ refine segmentation corresponding to\nthe extracted organ region of interest (ROI). We propose a coarse-to-fine\npipeline, which starts from the extraction of the global prior context of\nmultiple organs from 3D volumes using a low-resolution coarse network, followed\nby a fine phase that uses a single refined model to segment all abdominal\norgans instead of multiple organ corresponding models. We combine the\nanatomical prior with corresponding extracted patches to preserve the\nanatomical locations and boundary information for performing high-resolution\nsegmentation across all organs in a single model. To train and evaluate our\nmethod, a clinical research cohort consisting of 100 patient volumes with 13\norgans well-annotated is used. We tested our algorithms with 4-fold\ncross-validation and computed the Dice score for evaluating the segmentation\nperformance of the 13 organs. Our proposed method using single auto-context\noutperforms the state-of-the-art on 13 models with an average Dice score 84.58%\nversus 81.69% (p<0.0001).\n