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Segmentation of tubular structures in vascular imaging is a well studiedtask, although it is rare that we try to infuse knowledge of the tree-likestructure of the regions to be detected. Our work focuses on detecting theimportant landmarks in the vascular network (via CNN performing bothlocalization and classification of the points of interest) and representingvessels as the edges in some minimal distance tree graph. We leverage geodesicmethods relevant to the detection of vessels and their geometry, making use ofthe space of positions and orientations so that 2D vessels can be accuratelyrepresented as trees. We build our model to carry tracking on UltrasoundLocalization Microscopy (ULM) data, proposing to build a good cost function fortracking on this type of data. We also test our framework on synthetic and eyefundus data. Results show that scarcity of well annotated ULM data is anobstacle to localization of vascular landmarks but the Orientation Score builtfrom ULM data yields good geodesics for tracking blood vessels.

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