We propose a minimal path method to simultaneously compute segmentation masks
and extract centerlines of tubular structures with line-topology. Minimal path
methods are commonly used for the segmentation of tubular structures in a wide
variety of applications. Recent methods use features extracted by CNNs, and
often outperform methods using hand-tuned features. However, for CNN-based
methods, the samples used for training may be generated inappropriately, so
that they can be very different from samples encountered during inference. We
approach this discrepancy by introducing a novel iterative training scheme,
which enables generating better training samples specifically tailored for the
minimal path methods without changing existing annotations. In our method,
segmentation masks and centerlines are not determined after one another by
post-processing, but obtained using the same steps. Our method requires only
very few annotated training images. Comparison with seven previous approaches
on three public datasets, including satellite images and medical images, shows
that our method achieves state-of-the-art results both for segmentation masks
and centerlines.

我们提出了一种最小路径方法，用于同时计算具有线拓扑的管状结构的分割掩模并提取中心线。该方法通过引入一种新颖的迭代训练方案，使得能够生成更好的针对最小路径方法的训练样本，而不需改变现有的标注，从而解决了由于使用 CNN 的方法所生成的训练样本与推理时遇到的样本之间可能存在的差异。我们的方法在分割掩模和中心线方面取得了最先进的结果，并且仅需要非常少量的标注训练图像。

利用定制样本的迭代训练对管状结构进行分割

Segmentation of Tubular Structures Using Iterative Training with  Tailored Samples

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。