Abstract
For effective image segmentation, it is crucial to employ constraints informed by prior knowledge about the characteristics of the areas to be segmented to yield favorable segmentation outcomes. However, the existing methods have primarily focused on priors of specific properties or shapes, lacking consideration of the general global shape similarity from a Contour Flow (CF) perspective. Furthermore, naturally integrating this contour flow prior image segmentation model into the activation functions of deep convolutional networks through mathematical methods is currently unexplored. In this paper, we establish a concept of global shape similarity based on the premise that two shapes exhibit comparable contours. Furthermore, we mathematically derive a contour flow constraint that ensures the preservation of global shape similarity. We propose two implementations to integrate the constraint with deep neural networks. Firstly, the constraint is converted to a shape loss, which can be seamlessly incorporated into the training phase for any learning-based segmentation framework. Secondly, we add the constraint into a variational segmentation model and derive its iterative schemes for solution. The scheme is then unrolled to get the architecture of the proposed CFSSnet. Validation experiments on diverse datasets are conducted on classic benchmark deep network segmentation models. The results indicate a great improvement in segmentation accuracy and shape similarity for the proposed shape loss, showcasing the general adaptability of the proposed loss term regardless of specific network architectures. CFSSnet shows robustness in segmenting noise-contaminated images, and inherent capability to preserve global shape similarity.
Abstract (translated)
为了实现有效的图像分割，利用先前知识中关于待分割区域特征的约束以获得良好的分割结果至关重要。然而，现有的方法主要集中在特定属性或形状的先验信息上，而缺乏从轮廓流（Contour Flow, CF）角度考虑的一般全局形状相似性的考量。此外，通过数学方法将这种基于轮廓流的先验图像分割模型自然地整合到深度卷积网络的激活函数中尚未被探索。 在本文中，我们建立了一种基于两个形状具有可比性轮廓这一前提的全球形状相似度的概念，并从数学上推导出一种保持全局形状相似性的轮廓流约束。我们提出了两种将该约束与深层神经网络结合的方法：首先，我们将约束转换为一个形状损失函数，它可以无缝地融入任何学习型分割框架的训练阶段；其次，我们在变分分割模型中添加了约束，并为其推导出了求解方案。随后，我们展开了这个方案以获得提出的CFSSnet架构的设计。 在经典基准深度网络分割模型上对不同数据集进行了验证实验。结果显示，所提出的形状损失极大地提高了分割的准确性和形状相似性，展示了无论具体网络结构如何，该损失项具有普遍适应性的特点。此外，CFSSnet在处理噪声污染图像时表现出强大的鲁棒性，并且具备保持全局形状相似性的内在能力。
URL
https://arxiv.org/abs/2504.09384
