Automatic Cell Segmentation by Adaptive Thresholding (ACSAT) for Large-Scale Calcium Imaging Datasets

Abstract

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.

Precision calcium imaging of dense neural populations via a cell body-targeted calcium indicator

“…To identify the regions of interest (ROIs) that represent neurons, we first generated time-collapsed images by subtracting the average intensity value of each pixel over all videos from its maximum intensity. We then applied ACSAT (Shen et al, 2018) to generate ROIs with the following parameters: iteration=2, minimum size=50 pixels, and maximum size=300 pixels. In brief, ACSAT is a threshold-based ROI segmentation algorithm that adaptively adjusts the threshold at both global and local levels to capture ROIs with various intensities.…”

Distinct neuronal populations contribute to trace conditioning and extinction learning in the hippocampal CA1

“…ROIs were matched to one another, spatial ROI maps were co-registered using framewise cross-correlation. ROIs were then matched using a greedy method that required the centroid of cells to be within 50 pixels of one another and had to have at least 50% of their pixels overlap, as published previously 59 . Cells that did not meet both of those criteria were removed from the matched dataset for comparison.…”

Comparative Effects of Event Detection Methods on the Analysis and Interpretation of Ca2+ Imaging Data

“…This caveat, however, was not explicitly tested in our study. In the context of an automated analysis pipeline, this activity-based approach lends some confidence that only active pixels are included within the ROI outlines and is distinct from approaches that identify ROIs based on thresholds or watershed transforms applied to an average or maximal imaged field ( Wong et al, 2010 ; Shen et al, 2018 ) or based on secondary fluorescent markers ( Wardill et al, 2013 ). To further reduce potential signal contamination, we removed the local, rather than global, neuropil signal from each individual ROI as has been done in previous studies ( Keemink et al, 2018 ; Pnevmatikakis, 2019 ; Soltanian-Zadeh et al, 2019 ).…”