Skip to main content
main-content
Erschienen in: Brain Structure and Function 1/2018

09.11.2017 | Methods Paper

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

verfasst von: Jiangheng Guan, Jingcheng Li, Shanshan Liang, Ruijie Li, Xingyi Li, Xiaozhe Shi, Ciyu Huang, Jianxiong Zhang, Junxia Pan, Hongbo Jia, Le Zhang, Xiaowei Chen, Xiang Liao

Erschienen in: Brain Structure and Function | Ausgabe 1/2018

Einloggen, um Zugang zu erhalten

Abstract

Two-photon Ca2+ 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 Ca2+ 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 Ca2+ 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.
Literatur
Zurück zum Zitat Apthorpe N, Riordan A, Aguilar R, Homann J, Gu Y, Tank D, Seung HS (2016) Automatic neuron detection in calcium imaging data using convolutional networks. In: 2016 Advances in neural information processing systems, pp 3270–3278 Apthorpe N, Riordan A, Aguilar R, Homann J, Gu Y, Tank D, Seung HS (2016) Automatic neuron detection in calcium imaging data using convolutional networks. In: 2016 Advances in neural information processing systems, pp 3270–3278
Zurück zum Zitat Denk W, Delaney KR, Gelperin A, Kleinfeld D, Strowbridge BW, Tank DW, Yuste R (1994) Anatomical and functional imaging of neurons using 2-photon laser scanning microscopy. J Neurosci Methods 54(2):151–162 CrossRefPubMed Denk W, Delaney KR, Gelperin A, Kleinfeld D, Strowbridge BW, Tank DW, Yuste R (1994) Anatomical and functional imaging of neurons using 2-photon laser scanning microscopy. J Neurosci Methods 54(2):151–162 CrossRefPubMed
Zurück zum Zitat Diego F, Hamprecht FA (2013) Learning multi-level sparse representations. In: 2013 Advances in neural information processing systems, pp 818–826 Diego F, Hamprecht FA (2013) Learning multi-level sparse representations. In: 2013 Advances in neural information processing systems, pp 818–826
Zurück zum Zitat Meijering E (2012) Cell segmentation: 50 years down the road. IEEE Signal Process Mag 29(5):140–145 CrossRef Meijering E (2012) Cell segmentation: 50 years down the road. IEEE Signal Process Mag 29(5):140–145 CrossRef
Zurück zum Zitat Vedaldi A, Lenc K (2015) Matconvnet: convolutional neural networks for matlab. In: 2015 proceedings of the 23rd ACM international conference on multimedia, pp 689–692 Vedaldi A, Lenc K (2015) Matconvnet: convolutional neural networks for matlab. In: 2015 proceedings of the 23rd ACM international conference on multimedia, pp 689–692
Zurück zum Zitat Xu K, Su H, Zhu J, Guan J-S, Zhang B (2016b) Neuron segmentation based on CNN with semi-supervised regularization. In: 2016 proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 20–28 Xu K, Su H, Zhu J, Guan J-S, Zhang B (2016b) Neuron segmentation based on CNN with semi-supervised regularization. In: 2016 proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 20–28
Metadaten
Titel
NeuroSeg: automated cell detection and segmentation for in vivo two-photon Ca2+ imaging data
verfasst von
Jiangheng Guan
Jingcheng Li
Shanshan Liang
Ruijie Li
Xingyi Li
Xiaozhe Shi
Ciyu Huang
Jianxiong Zhang
Junxia Pan
Hongbo Jia
Le Zhang
Xiaowei Chen
Xiang Liao
Publikationsdatum
09.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Brain Structure and Function / Ausgabe 1/2018
Print ISSN: 1863-2653
Elektronische ISSN: 1863-2661
DOI
https://doi.org/10.1007/s00429-017-1545-5