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.

本论文提出了一种新的轮廓演化定义，称为Recurrent Level Set (RLS)，通过Gated Recurrent Unit在变分LS函数的能量最小化下进行，以弥补经典变分LS方法在处理多实例对象和迭代次数影响等方面的局限性，并将其扩展为Contextual RLS (CRLS)以解决野外语义分割问题分。实验结果表明，与经典的变分LS方法相比，我们提出的RLS方法能提高计算时间和分割精度，而完全端到端系统CRLS与当前最先进的语义分割方法的性能相当。

将水平集重新构造为深度循环神经网络方法以进行语义分割