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The difficulty of obtaining annotations to build training databases stillslows down the adoption of recent deep learning approaches for biomedical imageanalysis. In this paper, we show that we can train a Deep Net to perform 3Dvolumetric delineation given only 2D annotations in Maximum IntensityProjections (MIP). As a consequence, we can decrease the amount of time spentannotating by a factor of two while maintaining similar performance.  Our approach is inspired by space carving, a classical technique ofreconstructing complex 3D shapes from arbitrarily-positioned cameras. We willdemonstrate its effectiveness on 3D light microscopy images of neurons andretinal blood vessels and on Magnetic Resonance Angiography (MRA) brain scans.

Tracing in 2D to Reduce the Annotation Effort for 3D Deep Delineation