Segmentation of tubular structures in vascular imaging is a well studied
task, although it is rare that we try to infuse knowledge of the tree-like
structure of the regions to be detected. Our work focuses on detecting the
important landmarks in the vascular network (via CNN performing both
localization and classification of the points of interest) and representing
vessels as the edges in some minimal distance tree graph. We leverage geodesic
methods relevant to the detection of vessels and their geometry, making use of
the space of positions and orientations so that 2D vessels can be accurately
represented as trees. We build our model to carry tracking on Ultrasound
Localization Microscopy (ULM) data, proposing to build a good cost function for
tracking on this type of data. We also test our framework on synthetic and eye
fundus data. Results show that scarcity of well annotated ULM data is an
obstacle to localization of vascular landmarks but the Orientation Score built
from ULM data yields good geodesics for tracking blood vessels.

通过使用卷积神经网络对感兴趣的点进行定位和分类，并将血管表示为一些最小距离树状图的边，基于地形测量方法检测血管及其几何特征，我们的研究聚焦于在血管网络中检测重要的地标，并利用位置和方向的空间，准确地将 2D 血管表示为树状结构。我们构建了适用于超声定位显微镜（ULM）数据跟踪的模型，提出在这种类型数据上构建良好的成本函数用于跟踪。我们还对合成数据和眼底图像数据进行了测试。结果表明，缺乏良好注释的 ULM 数据是血管地标定位的障碍，但基于 ULM 数据建立的方向得分对于追踪血管具有良好的测地线效果。

利用 CNN 和测地线拟合树模型以跟踪血管并应用于超声定位显微镜数据

Fitting tree model with CNN and geodesics to track vesselsand  application to Ultrasound Localization Microscopy data

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。

内嵌 CNN 的渐进极简路径法

Progressive Minimal Path Method with Embedded CNN

Accurate segmentation of tubular, network-like structures, such as vessels,
neurons, or roads, is relevant to many fields of research. For such structures,
the topology is their most important characteristic; particularly preserving
connectedness: in the case of vascular networks, missing a connected vessel
entirely alters the blood-flow dynamics. We introduce a novel similarity
measure termed centerlineDice (short clDice), which is calculated on the
intersection of the segmentation masks and their (morphological) skeleta. We
theoretically prove that clDice guarantees topology preservation up to homotopy
equivalence for binary 2D and 3D segmentation. Extending this, we propose a
computationally efficient, differentiable loss function (soft-clDice) for
training arbitrary neural segmentation networks. We benchmark the soft-clDice
loss on five public datasets, including vessels, roads and neurons (2D and 3D).
Training on soft-clDice leads to segmentation with more accurate connectivity
information, higher graph similarity, and better volumetric scores.

介绍了一种新的相似度度量方法 centerlineDice 以及通过基于相似度度量的训练方法 soft-clDice 实现了对管状结构，如血管和神经元等的更精确分割和更好的连通性信息提取。