Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data
This work addresses the challenge of efficiently segmenting neuronal structures in brain imaging for neuroscience research, offering a practical solution to reduce annotation effort while maintaining accuracy.
The paper tackled the problem of semantic segmentation of neuronal bodies in 3D fluorescence microscopy by proposing a two-phase training strategy for 3D CNNs using sparse 2D annotations, which improved surface reconstruction accuracy compared to 2D CNNs without requiring extensive manual volumetric annotations.
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.