  Coarse-to-fine models and cascade segmentation architectures are widely
adopted to solve the problem of large scale variations in medical image
segmentation. However, those methods have two primary limitations: the
first-stage segmentation becomes a performance bottleneck; the lack of overall
differentiability makes the training process of two stages asynchronous and
inconsistent. In this paper, we propose a differentiable two-stage network
architecture to tackle these problems. In the first stage, a localization
network (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in
the second stage, a segmentation network (S-Net) performs fine segmentation on
the recalibrated RoIs; a RoI recalibration module between L-Net and S-Net
eliminating the inconsistencies. Experimental results on the public dataset
show that our method outperforms state-of-the-art coarse-to-fine models with
negligible computation overheads.
