We propose Path-CNN, a method for the segmentation of centerlines of tubular
structures by embedding convolutional neural networks (CNNs) into the
progressive minimal path method. Minimal path methods are widely used for
topology-aware centerline segmentation, but usually these methods rely on weak,
hand-tuned image features. In contrast, CNNs use strong image features which
are learned automatically from images. But CNNs usually do not take the
topology of the results into account, and often require a large amount of
annotations for training. We integrate CNNs into the minimal path method, so
that both techniques benefit from each other: CNNs employ learned image
features to improve the determination of minimal paths, while the minimal path
method ensures the correct topology of the segmented centerlines, provides
strong geometric priors to increase the performance of CNNs, and reduces the
amount of annotations for the training of CNNs significantly. Our method has
lower hardware requirements than many recent methods. Qualitative and
quantitative comparison with other methods shows that Path-CNN achieves better
performance, especially when dealing with tubular structures with complex
shapes in challenging environments.

提出了一种名为 Path-CNN 的方法，该方法将卷积神经网络嵌入到渐进式最小路径方法中，用于检测管状结构中心线的分割，可以在更少的注释数据和低硬件要求下达到更好的性能。