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