NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data

Two-photon Ca 2+ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In parti...

Full description

Saved in:
Bibliographic Details
Published in:Brain Structure and Function 2018-01, Vol.223 (1), p.519-533
Main Authors: Guan, Jiangheng, Li, Jingcheng, Liang, Shanshan, Li, Ruijie, Li, Xingyi, Shi, Xiaozhe, Huang, Ciyu, Zhang, Jianxiong, Pan, Junxia, Jia, Hongbo, Zhang, Le, Chen, Xiaowei, Liao, Xiang
Format: Article
Language:English
Subjects:
Automation
Biomedical and Life Sciences
Biomedicine
Brain mapping
Calcium imaging
Cell Biology
Data processing
Image processing
Methods Paper
Neuroimaging
Neurology
Neurosciences
Segmentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Two-photon Ca 2+ imaging has become a popular approach for monitoring neuronal population activity with cellular or subcellular resolution in vivo. This approach allows for the recording of hundreds to thousands of neurons per animal and thus leads to a large amount of data to be processed. In particular, manually drawing regions of interest is the most time-consuming aspect of data analysis. However, the development of automated image analysis pipelines, which will be essential for dealing with the likely future deluge of imaging data, remains a major challenge. To address this issue, we developed NeuroSeg, an open-source MATLAB program that can facilitate the accurate and efficient segmentation of neurons in two-photon Ca 2+ imaging data. We proposed an approach using a generalized Laplacian of Gaussian filter to detect cells and weighting-based segmentation to separate individual cells from the background. We tested this approach on an in vivo two-photon Ca 2+ imaging dataset obtained from mouse cortical neurons with differently sized view fields. We show that this approach exhibits superior performance for cell detection and segmentation compared with the existing published tools. In addition, we integrated the previously reported, activity-based segmentation into our approach and found that this combined method was even more promising. The NeuroSeg software, including source code and graphical user interface, is freely available and will be a useful tool for in vivo brain activity mapping.
ISSN:1863-2653
1863-2661
0340-2061
DOI:10.1007/s00429-017-1545-5