Skip to main content

Main menu

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Blog
    • Collections
    • Podcast
  • TOPICS
    • Cognition and Behavior
    • Development
    • Disorders of the Nervous System
    • History, Teaching and Public Awareness
    • Integrative Systems
    • Neuronal Excitability
    • Novel Tools and Methods
    • Sensory and Motor Systems
  • ALERTS
  • FOR AUTHORS
  • ABOUT
    • Overview
    • Editorial Board
    • For the Media
    • Privacy Policy
    • Contact Us
    • Feedback
  • SUBMIT

User menu

Search

  • Advanced search
eNeuro
eNeuro

Advanced Search

 

  • HOME
  • CONTENT
    • Early Release
    • Featured
    • Current Issue
    • Issue Archive
    • Blog
    • Collections
    • Podcast
  • TOPICS
    • Cognition and Behavior
    • Development
    • Disorders of the Nervous System
    • History, Teaching and Public Awareness
    • Integrative Systems
    • Neuronal Excitability
    • Novel Tools and Methods
    • Sensory and Motor Systems
  • ALERTS
  • FOR AUTHORS
  • ABOUT
    • Overview
    • Editorial Board
    • For the Media
    • Privacy Policy
    • Contact Us
    • Feedback
  • SUBMIT
PreviousNext
Research ArticleMethods/New Tools, Novel Tools and Methods

ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data

Stephanie Reynolds, Therese Abrahamsson, Renaud Schuck, P. Jesper Sjöström, Simon R. Schultz and Pier Luigi Dragotti
eNeuro 16 October 2017, 4 (5) ENEURO.0012-17.2017; https://doi.org/10.1523/ENEURO.0012-17.2017
Stephanie Reynolds
1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
2Centre for Neurotechnology, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Therese Abrahamsson
3Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montréal General Hospital, Montréal, Québec H3G 1A4, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Therese Abrahamsson
Renaud Schuck
2Centre for Neurotechnology, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
4Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
P. Jesper Sjöström
3Centre for Research in Neuroscience, Brain Repair and Integrative Neuroscience Program, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montréal General Hospital, Montréal, Québec H3G 1A4, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for P. Jesper Sjöström
Simon R. Schultz
2Centre for Neurotechnology, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
4Department of Bioengineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Simon R. Schultz
Pier Luigi Dragotti
1Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Pier Luigi Dragotti
  • Article
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Figure 1.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 1.

    A flow diagram of the main steps of the proposed segmentation algorithm: initialization (A–C), iterative updates of the estimate (D–G), and convergence (H-J). When cells are sufficiently far apart we can segment them independently, in this example, we focus on the isolated cell in the dashed box on the maximum intensity image in A. We make an initial estimate of the cell interior, from which we form the corresponding narrowband (B) and level set function ϕ (C). Based on the discrepancy between a pixel’s time course and the time courses of the interior and narrowband regions (D), we calculate the velocity of ϕ at each pixel (E). ϕ Evolves according to this velocity (G), which updates the location of the interior and narrowband (F). Final results for: one cell (H), the average signals from the corresponding interior and narrowband (I), and segmentation of all four cells (J).

  • Figure 2.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 2.

    ABLE detects cells with varying size, shape, and baseline intensity from mouse in vivo imaging data. The 236 detected ROIs are superimposed on the mean image of the imaging video (A). Extracted neuropil-corrected time series and corresponding ROIs are displayed for a subset of the detected regions (B). Cells with both stereotypical calcium transient activity (B, 1–9) and saturating fluorescence (B, 10–12) are detected. The performance of ABLE does not deteriorate due to intensity inhomogeneity: ROIs with baseline fluorescence from beneath the video median to just below saturation are detected (C). The area of detected regions varies (D) with the smallest ROIs corresponding to cross-sections of dendrites (E). Neighboring regions with sufficiently high correlation are merged (F), those with lower correlation are not merged (G). In F, we plot the ROIs before and after merging along with the corresponding neuropil-corrected time courses. In G, we plot the separate ROIs and the neuropil-corrected time courses. The proposed method naturally facilitates neuropil-correction, the removal of the weighted, local neuropil time course from the raw cellular time course (H).

  • Figure 3.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 3.

    ABLE demixes overlapping cells in real and simulated data. With high accuracy, we detect the true boundaries of overlapping cells from noisy simulated data, the detected contours for one realization of noise with SD (σ) 60 are plotted on the correlation image in A. Given an initialization on a fixed grid, displayed on the mean image in D, we detect the true cell boundaries with success rate of at least 99% for σ < 90 (B). The central marker and box edges in B indicate the median and the 25th and 75th percentiles, respectively. For noise level reference, we plot the average time course from inside the green contour in A at various levels (C). ABLE demixes overlapping cells in real GCaMP6s mouse in vivo data, detected boundaries are superimposed on the mean image (E, F) and correlation image (G), respectively.

  • Figure 4.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 4.

    ABLE detects synchronously spiking, densely packed cells from mouse in vitro imaging data. The boundaries of the 207 detected ROIs are superimposed on the thresholded maximum intensity image (A) and the correlation image (D). For all correlation data we use Pearson’s correlation coefficient. ABLE detects ROIs that exhibit high correlation with the background (C) and neighboring synchronously spiking ROIs (B). B, Neuropil-corrected extracted time courses of the 207 ROIs (each plotted as a row of the matrix) along with the video mean raw activity and the time points of the electrical stimulations. C, Histogram of the correlation coefficient between the mean raw activity of the video and the extracted time series of each ROI. ABLE detected both active (E-G) and inactive ROIs (H, I). We display the contours of the two detected ROIs on the correlation image (E, H), the mean image (F, I) and the corresponding extracted time courses (G, J).

  • Figure 5.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 5.

    We compare the segmentation results of ABLE, CNMF (Pnevmatikakis et al., 2016), and Suite2p (Pachitariu et al., 2016) on a manually labeled dataset from the Neurofinder Challenge. On the correlation image, we plot the boundaries of the manually labeled cells color-coded by the combination of algorithms that detected them (A), undetected cells are indicated by a white contour. Suite2p detected the highest proportion of manually labeled cells (B), whereas ABLE had the lowest fall-out rate (C), which is the percentage of detected regions not present in the manual labels. Some algorithm-detected ROIs that were not present in the manual labels are detected by multiple algorithms (D) and have time courses which exhibit stereotypical calcium transient activity (E). The correlation image in D is thresholded to enhance visibility of local peaks in correlation. In E, we plot the extracted time courses of the ROIs in D.

  • Figure 6.
    • Download figure
    • Open in new tab
    • Download powerpoint
    Figure 6.

    Spikes are detected from ABLE-extracted time courses with high accuracy. On an in vitro dataset (21 imaging videos, each 30 s long), we demonstrate spike detection performance compared to electrophysiological ground truth on time courses extracted from cells segmented by ABLE. We plot the labeled cells (A) and corresponding boundaries detected by ABLE (B) on the mean image of one imaging video. The extracted cellular time courses and detected spikes are plotted in C. Spike detection was performed with an existing algorithm (Reynolds et al., 2016; Oñativia et al., 2013). On average over all videos, 78% of spikes are detected with a precision of 88% D.

Tables

  • Figures
    • View popup
    Table 1.

    Runtime (minutes) on synthetic data of size 512 × 512 × T

    Number of cells
    25125225
    Number of frames (T)1001.16.511.2
    10001.36.512.7
    • On synthetic data with dimensions 512 × 512 × T, the runtime of ABLE (minutes) increases linearly with the number of cells and is not significantly affected by increasing number of frames, T. Runtime was measured on a PC with 3.4 GHz Intel Core i7 CPU.

    • View popup
    Table 2.

    Number of iterations to convergence as cell density increases

    Number of neighbors01234
    Number of iterations3333353536
    • On synthetic data the average number of iterations to convergence, over 100 realizations of noisy data, marginally increases as the number of cells in a given cell’s narrowband (“neighboring cells”) increases.

    • View popup
    Table 3.

    Algorithm success rate on manually labeled dataset

    Success rate (%)Precision (%)Recall (%)
    ABLE67.567.567.5
    CNMF63.460.766.5
    Suite2p63.756.573.1
    • On a manually labeled dataset from the Neurofinder Challenge, we compare the performance of three segmentation algorithms: ABLE, CNMF (Pnevmatikakis et al., 2016), and Suite2p (Pachitariu et al., 2016), using the manual labels as ground truth.

Back to top

In this issue

eneuro: 4 (5)
eNeuro
Vol. 4, Issue 5
September/October 2017
  • Table of Contents
  • Index by author
Email

Thank you for sharing this eNeuro article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
(Your Name) has forwarded a page to you from eNeuro
(Your Name) thought you would be interested in this article in eNeuro.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
View Full Page PDF
Citation Tools
ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
Stephanie Reynolds, Therese Abrahamsson, Renaud Schuck, P. Jesper Sjöström, Simon R. Schultz, Pier Luigi Dragotti
eNeuro 16 October 2017, 4 (5) ENEURO.0012-17.2017; DOI: 10.1523/ENEURO.0012-17.2017

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Respond to this article
Share
ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data
Stephanie Reynolds, Therese Abrahamsson, Renaud Schuck, P. Jesper Sjöström, Simon R. Schultz, Pier Luigi Dragotti
eNeuro 16 October 2017, 4 (5) ENEURO.0012-17.2017; DOI: 10.1523/ENEURO.0012-17.2017
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Significance Statement
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Footnotes
    • References
    • Synthesis
    • Author Response
  • Figures & Data
  • Info & Metrics
  • eLetters
  • PDF

Keywords

  • Active Contour
  • calcium imaging
  • fluorescence microscopy
  • Level Set Method
  • segmentation

Responses to this article

Respond to this article

Jump to comment:

No eLetters have been published for this article.

Related Articles

Cited By...

More in this TOC Section

Methods/New Tools

  • Superficial Bound of the Depth Limit of Two-Photon Imaging in Mouse Brain
  • A Toolbox of Criteria for Distinguishing Cajal–Retzius Cells from Other Neuronal Types in the Postnatal Mouse Hippocampus
  • Assessment of Spontaneous Neuronal Activity In Vitro Using Multi-Well Multi-Electrode Arrays: Implications for Assay Development
Show more Methods/New Tools

Novel Tools and Methods

  • Superficial Bound of the Depth Limit of Two-Photon Imaging in Mouse Brain
  • A Toolbox of Criteria for Distinguishing Cajal–Retzius Cells from Other Neuronal Types in the Postnatal Mouse Hippocampus
  • Assessment of Spontaneous Neuronal Activity In Vitro Using Multi-Well Multi-Electrode Arrays: Implications for Assay Development
Show more Novel Tools and Methods

Subjects

  • Novel Tools and Methods
  • Home
  • Alerts
  • Follow SFN on BlueSky
  • Visit Society for Neuroscience on Facebook
  • Follow Society for Neuroscience on Twitter
  • Follow Society for Neuroscience on LinkedIn
  • Visit Society for Neuroscience on Youtube
  • Follow our RSS feeds

Content

  • Early Release
  • Current Issue
  • Latest Articles
  • Issue Archive
  • Blog
  • Browse by Topic

Information

  • For Authors
  • For the Media

About

  • About the Journal
  • Editorial Board
  • Privacy Notice
  • Contact
  • Feedback
(eNeuro logo)
(SfN logo)

Copyright © 2025 by the Society for Neuroscience.
eNeuro eISSN: 2373-2822

The ideas and opinions expressed in eNeuro do not necessarily reflect those of SfN or the eNeuro Editorial Board. Publication of an advertisement or other product mention in eNeuro should not be construed as an endorsement of the manufacturer’s claims. SfN does not assume any responsibility for any injury and/or damage to persons or property arising from or related to any use of any material contained in eNeuro.