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

被引：9

作者：

Mazzamuto, G. ^{[1
]}

Costantini, I ^{[1
]}

Neri, M. ^{[2
]}

Roffilli, M. ^{[2
]}

Silvestri, L. ^{[1
,3
]}

Pavone, F. S. ^{[1
,3
,4
]}

机构：

[1] European Lab Nonlinear Spect LENS, Via Nello Carrara 1, I-50019 Sesto Fiorentino, FI, Italy

[2] Bioret Srl, Corte Zavattini 21, I-47522 Cesena, FC, Italy

[3] Natl Res Council INO CNR, Natl Inst Opt, Via Nello Carrara 1, I-50019 Sesto Fiorentino, FI, Italy

[4] Univ Florence, Dept Phys & Astron, Via G Sansone 1, I-50019 Sesto Fiorentino, FI, Italy

来源：

APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2018 | 2018年 / 10784卷

基金：

欧洲研究理事会;

关键词：

Segmentation; Brain images; Convolutional neural network;

D O I：

10.1007/978-3-319-77538-8_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：78 / 85

页数：8

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