Quantitative analysis of brain cytoarchitecture requires effective and efficient segmentation of the raw images. This task is highly demanding from an algorithmic point of view, because of the inherent variations of contrast and intensity in the different areas of the specimen, and of the very large size of the datasets to be processed. Here, we report a machine vision approach based on Convolutional Neural Networks (CNN) for the near real-time segmentation of neurons in three-dimensional images with high specificity and sensitivity. This instrument, together with high-throughput sample preparation and imaging, can lay the basis for a quantitative revolution in neuroanatomical studies.

Automatic Segmentation of Neurons in 3D Samples of Human Brain Cortex / Mazzamuto G.; Costantini I.; Neri M.; Roffilli M.; Silvestri L.; Pavone F.S.. - ELETTRONICO. - (2018), pp. 78-85. [10.1007/978-3-319-77538-8_6]

Automatic Segmentation of Neurons in 3D Samples of Human Brain Cortex

Mazzamuto G.
;
Costantini I.;Silvestri L.;Pavone F. S.
2018

Abstract

Quantitative analysis of brain cytoarchitecture requires effective and efficient segmentation of the raw images. This task is highly demanding from an algorithmic point of view, because of the inherent variations of contrast and intensity in the different areas of the specimen, and of the very large size of the datasets to be processed. Here, we report a machine vision approach based on Convolutional Neural Networks (CNN) for the near real-time segmentation of neurons in three-dimensional images with high specificity and sensitivity. This instrument, together with high-throughput sample preparation and imaging, can lay the basis for a quantitative revolution in neuroanatomical studies.
2018
978-3-319-77537-1
978-3-319-77538-8
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
78
85
Goal 3: Good health and well-being for people
Goal 9: Industry, Innovation, and Infrastructure
Mazzamuto G.; Costantini I.; Neri M.; Roffilli M.; Silvestri L.; Pavone F.S.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1162620
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