  Many biological and medical tasks require the delineation of 3D curvilinear
structures such as blood vessels and neurites from image volumes. This is
typically done using neural networks trained by minimizing voxel-wise loss
functions that do not capture the topological properties of these structures.
As a result, the connectivity of the recovered structures is often wrong, which
lessens their usefulness. In this paper, we propose to improve the 3D
connectivity of our results by minimizing a sum of topology-aware losses on
their 2D projections. This suffices to increase the accuracy and to reduce the
annotation effort required to provide the required annotated training data.
Code is available at https://github.com/doruk-oner/ConnectivityOnProjections.
