  Semantic segmentation of neuronal structures in 3D high-resolution
fluorescence microscopy imaging of the human brain cortex can take advantage of
bidimensional CNNs, which yield good results in neuron localization but lead to
inaccurate surface reconstruction. 3D CNNs, on the other hand, would require
manually annotated volumetric data on a large scale and hence considerable
human effort. Semi-supervised alternative strategies which make use only of
sparse annotations suffer from longer training times and achieved models tend
to have increased capacity compared to 2D CNNs, needing more ground truth data
to attain similar results. To overcome these issues we propose a two-phase
strategy for training native 3D CNN models on sparse 2D annotations where
missing labels are inferred by a 2D CNN model and combined with manual
annotations in a weighted manner during loss calculation.
