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

Enforcing connectivity of 3D linear structures using their 2D projections