  Variational Level Set (LS) has been a widely used method in medical
segmentation. However, it is limited when dealing with multi-instance objects
in the real world. In addition, its segmentation results are quite sensitive to
initial settings and highly depend on the number of iterations. To address
these issues and boost the classic variational LS methods to a new level of the
learnable deep learning approaches, we propose a novel definition of contour
evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under
the energy minimization of a variational LS functional. The curve deformation
process in RLS is formed as a hidden state evolution procedure and updated by
minimizing an energy functional composed of fitting forces and contour length.
By sharing the convolutional features in a fully end-to-end trainable
framework, we extend RLS to Contextual RLS (CRLS) to address semantic
segmentation in the wild. The experimental results have shown that our proposed
RLS improves both computational time and segmentation accuracy against the
classic variations LS-based method, whereas the fully end-to-end system CRLS
achieves competitive performance compared to the state-of-the-art semantic
segmentation approaches.
