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

• URL: http://arxiv.org/abs/2009.00029v2
• Date: Wed, 2 Sep 2020 00:37:53 GMT
• Title: Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data
• Authors: Filippo Maria Castelli, Matteo Roffilli, Giacomo Mazzamuto, Irene Costantini, Ludovico Silvestri and Francesco Saverio Pavone
• Abstract summary: Bidimensional CNNs yield good results in neuron localization but lead to inaccurate surface reconstruction. 3D CNNs would require manually annotated data on a large scale and hence considerable human effort. We propose a two-phase strategy for training native 3D CNN models on sparse 2D annotations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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