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.

翻译：血管成像中管状结构的分割是一项研究充分的任务，但鲜有研究尝试将待检测区域的树状结构知识融入其中。本工作聚焦于通过卷积神经网络（CNN）对血管网络中的重要地标进行定位与分类，并利用最小距离树图的边来表示血管。我们借鉴适用于血管检测及其几何结构的测地线方法，利用位置与方向空间使得二维血管能精确表示为树状结构。针对超声定位显微镜（ULM）数据，我们构建了追踪模型，并为此类数据设计了有效的代价函数。同时，我们在合成数据和眼底图像数据上测试了该框架。结果表明，充分标注的ULM数据稀缺是血管地标定位的主要障碍，但基于ULM数据构建的方向得分能生成用于血管追踪的高质量测地线。