Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data

@inproceedings{Castelli2020SemanticSO,
  title={Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data},
  author={Filippo Maria Castelli and Matteo Roffilli and Giacomo Mazzamuto and Irene Costantini and Ludovico Silvestri and Francesco Saverio Pavone},
  booktitle={LOD},
  year={2020}
}
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… Expand

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