NeuroGPS: automated localization of neurons for brain circuits using L1 minimization model

被引：57

作者：

Quan, Tingwei ^{[1
,2
,3
]}

Zheng, Ting ^{[1
,2
]}

Yang, Zhongqing ^{[1
,2
]}

Ding, Wenxiang ^{[1
,2
]}

Li, Shiwei ^{[1
,2
]}

Li, Jing ^{[1
,2
]}

Zhou, Hang ^{[1
,2
]}

Luo, Qingming ^{[1
,2
]}

Gong, Hui ^{[1
,2
]}

Zeng, Shaoqun ^{[1
,2
]}

机构：

[1] Huazhong Univ Sci & Technol, Britton Chance Ctr Biomed Photon, Wuhan Natl Lab Optoelect, Wuhan 430074, Peoples R China

[2] Huazhong Univ Sci & Technol, Dept Biomed Engn, MoE Key Lab Biomed Photon, Wuhan 430074, Peoples R China

[3] Hubei Univ Educ, Sch Math & Econ, Wuhan 430205, Peoples R China

来源：

SCIENTIFIC REPORTS | 2013年 / 3卷

关键词：

QUANTITATIVE-ANALYSIS; CELL-NUCLEI; SEGMENTATION; RECONSTRUCTION; TOMOGRAPHY; CHALLENGES; ALGORITHM; IMAGES; ATLAS; NICHE;

D O I：

10.1038/srep01414

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Drawing the map of neuronal circuits at microscopic resolution is important to explain how brain works. Recent progresses in fluorescence labeling and imaging techniques have enabled measuring the whole brain of a rodent like a mouse at submicron-resolution. Considering the huge volume of such datasets, automatic tracing and reconstruct the neuronal connections from the image stacks is essential to form the large scale circuits. However, the first step among which, automated location the soma across different brain areas remains a challenge. Here, we addressed this problem by introducing L1 minimization model. We developed a fully automated system, NeuronGlobalPositionSystem (NeuroGPS) that is robust to the broad diversity of shape, size and density of the neurons in a mouse brain. This method allows locating the neurons across different brain areas without human intervention. We believe this method would facilitate the analysis of the neuronal circuits for brain function and disease studies.

引用

页数：7