  Accurate and robust segmentation of small organs in whole-body MRI is
difficult due to anatomical variation and class imbalance. Recent deep network
based approaches have demonstrated promising performance on abdominal
multi-organ segmentations. However, the performance on small organs is still
suboptimal as these occupy only small regions of the whole-body volumes with
unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy
is a common approach to alleviate this problem, however, this might miss useful
contextual information. We propose a two-stage approach with weighting schemes
based on auto-context and spatial atlas priors. Our experiments show that the
proposed approach can boost the segmentation accuracy of multiple small organs
in whole-body MRI scans.
