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