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.

通过将图割方法与深度学习网络相结合，本研究提出了一种新颖的残差图割损失和准残差连接，实现了有效的特征学习和导向图割分割模型的梯度反向传播，在数据集上验证了良好的分割准确性和对抗性攻击的提高。