Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans. However, the segmentation accuracy of small organs (e.g., pancreas) or neoplasms (e.g., pancreatic cyst) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupy a large fraction of the input volume. In this chapter, we propose two coarse-to-fine mechanisms which use prediction from the first (coarse) stage to shrink the input region for the second (fine) stage. More specifically, the two stages in the first method are trained individually in a step-wise manner, so that the entire input region and the region cropped according to the bounding box are treated separately. While the second method inserts a saliency transformation module between the two stages so that the segmentation probability map from the previous iteration can be repeatedly converted as spatial weights to the current iteration. In training, it allows joint optimization over the deep networks. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments are performed on several CT datasets, including NIH pancreas, JHMI multi-organ, and JHMI pancreatic cyst dataset. Our proposed approach gives strong results in terms of DSC.