Low Computational-cost Cell Detection Method for Calcium Imaging Data

Abstract

The rapid progress of calcium imaging has reached a point where the activity of tens of thousands of cells can be recorded simultaneously. However, the huge amount of data in such records makes it difficult to carry out cell detection manually. Consequently, because the cell detection is the first step of multicellular data analysis, there is a pressing need for automatic cell detection methods for large-scale image data. Automatic cell detection algorithms have been pioneered by a handful of research groups. Such algorithms, however, assume a conventional field of view (FOV) (i.e. 512 x 512 pixels) and need a significantly higher computational power for a wider FOV to work within a practical period of time. To overcome this issue, we propose a method called low computational-cost cell detection (LCCD), which can complete its processing even on the latest ultra-large FOV data within a practical period of time. We compared it with two previously proposed methods, constrained non-negative matrix factorization (CNMF) and Suite2P. We found that LCCD makes it possible to detect cells from a hugeamount of high-density imaging data within a shorter period of time and with an accuracy comparable to or better than those of CNMF and Suite2P.

Fast, cell-resolution, contiguous-wide two-photon imaging to reveal functional network architectures across multi-modal cortical areas

“…We monitored the Ca 2+ activities of $16,000 cortical neurons from layer 2 (100-200 mm below the cortical surface) spanning 15 sensory-motor and higher-order brain areas in head-fixed awake mice (Figure 5A). An algorithm called the lowcomputational-cost cell detection (LCCD) (Ito et al, 2019) was applied to extract the Ca 2+ activity from each neuron (Figure 5B for clarification of ROIs and neurons, see also STAR Methods for the section ''Image analysis'' and Figure 5C for examples of Ca 2+ activity randomly selected from the ROIs). We were able to monitor movement-related and unrelated spontaneous Ca 2+ signals from a large sample (Figure 5D).…”

“…Brain motions of the recorded imaging data (8,000 frames) were corrected using the ImageJ plugin ''Image Stabilizer.'' We automatically detected the active neurons using methods described in our previous studies (Ito et al, 2019). The regions of interest (ROIs) were detected within each of the 500 frames using the following six steps.…”

Fast scanning high optical invariant two-photon microscopy for monitoring a large neural network activity with cellular resolution

“…Brain motion of the recorded imaging data (8,000 frames) was corrected using the ImageJ plugin "Image Stabilizer". We automatically detected active neurons using methods described within our studies (20). The regions of interest (ROIs) were detected within each of the 500 frames using the following 6 steps.…”