gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic
  Segmentation
Hui Xie Weiyu Xu Ya Xing Wang John Buatti Xiaodong Wu
Binary semantic segmentation in computer vision is a fundamental problem. As
a model-based segmentation method, the graph-cut approach was one of the most
successful binary segmentation methods thanks to its global optimality
guarantee of the solutions and its practical polynomial-time complexity.
Recently, many deep learning (DL) based methods have been developed for this
task and yielded remarkable performance, resulting in a paradigm shift in this
field. To combine the strengths of both approaches, we propose in this study to
integrate the graph-cut approach into a deep learning network for end-to-end
learning. Unfortunately, backward propagation through the graph-cut module in
the DL network is challenging due to the combinatorial nature of the graph-cut
algorithm. To tackle this challenge, we propose a novel residual graph-cut loss
and a quasi-residual connection, enabling the backward propagation of the
gradients of the residual graph-cut loss for effective feature learning guided
by the graph-cut segmentation model. In the inference phase, globally optimal
segmentation is achieved with respect to the graph-cut energy defined on the
optimized image features learned from DL networks. Experiments on the public
AZH chronic wound data set and the pancreas cancer data set from the medical
segmentation decathlon (MSD) demonstrated promising segmentation accuracy, and
improved robustness against adversarial attacks.