Most existing CNNs-based segmentation methods rely on local appearances learned on the regular image grid, without consideration of the object global information. This article aims to embed the object global geometric information into a learning framework via the classical geodesic active contours (GAC). We propose a level set function (LSF) regression network, supervised by the segmentation ground truth, LSF ground truth and geodesic active contours, to not only generate the segmentation probabilistic map but also directly minimize the GAC energy functional in an end-to-end manner. With the help of geodesic active contours, the segmentation contour, embedded in the level set function, can be globally driven towards the image boundary to obtain lower energy, and the geodesic constraint can lead the segmentation result to have fewer outliers. Extensive experiments on four public datasets show that (1) compared with state-of-the-art (SOTA) learning active contour methods, our method can achieve significantly better performance; (2) compared with recent SOTA methods that are designed for reducing boundary errors, our method also outperforms them with more accurate boundaries; (3) compared with SOTA methods on two popular multi-class segmentation challenge datasets, our method can still obtain superior or competitive results in both organ and tumor segmentation tasks. Our study demonstrates that introducing global information by GAC can significantly improve segmentation performance, especially on reducing the boundary errors and outliers, which is very useful in applications such as organ transplantation surgical planning and multi-modality image registration where boundary errors can be very harmful.

中文翻译：

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学习测地线活动轮廓以将对象全局信息嵌入分段CNN中。

大多数现有的基于CNN的分割方法都依赖于在常规图像网格上学习到的局部外观，而无需考虑对象全局信息。本文旨在通过经典的测地线活动轮廓线（GAC）将对象的全局几何信息嵌入到学习框架中。我们提出了一个水平集函数（LSF）回归网络，由分割地面实况，LSF地面实况和测地线活动轮廓监督，不仅可以生成分割概率图，而且可以在端到端直接最小化GAC能量函数方式。借助测地线活动轮廓，嵌入到水平集功能中的分割轮廓可以朝图像边界全局驱动以获得较低的能量，而测地约束可以使分割结果具有更少的离群值。在四个公共数据集上进行的广泛实验表明：（1）与最先进的（SOTA）学习主动轮廓方法相比，我们的方法可以显着提高性能；（2）与最近为减少边界误差而设计的SOTA方法相比，我们的方法在边界更准确的情况下也优于它们；（3）与SOTA方法在两个流行的多类别分割挑战数据集上相比，我们的方法在器官和肿瘤分割任务中仍然可以获得优异或竞争的结果。我们的研究表明，GAC引入全局信息可以显着提高分割效果，尤其是在减少边界误差和离群值方面，