Without utilizing any high-level prior information about expected objects, purely low-level information such as intensity, color and texture does not provide the desired segmentations.

However, given a training set of arbitrary prior shapes, there remains an open problem of how to define an appropriate prior shape model to guide object segmentation

we focus on image segmentation, and propose a shape prior constraint term by deep learning to guide variational segmentation.