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 larger datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semi-manual 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. Within this manuscript 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 over 80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.

Significance Statement ACSAT aims at automatically segmenting cells in large-scale calcium imaging datasets. It is based on adaptive thresholding at both global and local levels and iteratively identifies individual neurons in a time-collapsed image. It is designed to address a variety of datasets, potentially involving variations in cell morphology and fluorescence intensity between different datasets. We demonstrate the effectiveness of ACSAT by testing it under a variety of conditions. For the simulated datasets with truth, ACSAT achieved recall and precision rates over 80% when the signal-to-noise ratio was no less than ∼24 dB. For the datasets from mouse hippocampus and striatum, ACSAT captured ∼80% of human-identified ROIs and even detected some low-intensity neurons that were initially undetected by human referees.