  Tree-like structures, such as blood vessels, often express complexity at very
fine scales, requiring high-resolution grids to adequately describe their
shape. Such sparse morphology can alternately be represented by locations of
centreline points, but learning from this type of data with deep learning is
challenging due to it being unordered, and permutation invariant. In this work,
we propose a deep neural network that directly consumes unordered points along
the centreline of a branching structure, to identify the topology of the
represented structure in a single-shot. Key to our approach is the use of a
novel multi-task loss function, enabling instance segmentation of arbitrarily
complex branching structures. We train the network solely using synthetically
generated data, utilizing domain randomization to facilitate the transfer to
real 2D and 3D data. Results show that our network can reliably extract
meaningful information about branch locations, bifurcations and endpoints, and
sets a new benchmark for semantic instance segmentation in branching
structures.
