We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework. We train a class-specific convolutional neural network which predicts a vector pointing from the respective point on the evolving contour towards the closest point on the boundary of the object of interest. These predictions form a vector field which is then used for evolving the contour by the Sobolev active contour framework proposed by Sundaramoorthi et al. The resulting interactive segmentation method is very efficient in terms of required computational resources and can even be trained on comparatively small graphics cards. We evaluate the potential of the proposed method on both medical and non-medical challenge data sets, such as the STACOM data set and the PASCAL VOC 2012 data set.

该论文提出了一种结合深度、基于patch的表示和主动轮廓框架进行交互式边界提取的方法。作者使用了类别特定的卷积神经网络来训练向量预测器，以便指向感兴趣物体边界最接近的点，并使用Sobolev主动轮廓框架来演化轮廓。该方法在计算资源的使用方面非常高效，适合小型图形卡的使用，并在多个医学数据集和PASCAL VOC 2012数据集上进行了评估。

深度主动轮廓