  Deep learning techniques have successfully been employed in numerous computer
vision tasks including image segmentation. The techniques have also been
applied to medical image segmentation, one of the most critical tasks in
computer-aided diagnosis. Compared with natural images, the medical image is a
gray-scale image with low-contrast (even with some invisible parts). Because
some organs have similar intensity and texture with neighboring organs, there
is usually a need to refine automatic segmentation results. In this paper, we
propose an interactive deep refinement framework to improve the traditional
semantic segmentation networks such as U-Net and fully convolutional network.
In the proposed framework, we added a refinement network to traditional
segmentation network to refine the segmentation results.Experimental results
with public dataset revealed that the proposed method could achieve higher
accuracy than other state-of-the-art methods.
