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

ACSAT performance on simulated datasets. A1,A2, Recall (A1) and precision (A2) are plotted as a function of SNR and number of ROIs. Each dot corresponds to the ACSAT result for one of the 500 simulated datasets. A surface was fitted to these dots for visualization. The black vertical plane corresponds to the SNR of the hippocampus A dataset. B, Six examples of simulated time-collapsed images, labeled a–f, correspond to the dots labeled a–f in A1 and A2.

Hippocampus dataset A and ROIs identified by ACSAT. A, The time-collapsed image of hippocampus dataset A and zoom-in images (Ai, Aii, and Aiii, corresponding to the gray boxes). B, ACSAT-determined ROIs from multiple iterations overlying on the time-collapsed image (red, yellow, green, and blue outline corresponds to iteration 1, 2, 3, and 4, respectively). The fourth iteration (blue) is shown for comparison although ACSAT terminated at iteration 3 (red, yellow, and green).

Striatum dataset and ROIs identified by ACSAT. A, The time-collapsed image of striatum dataset and zoom-in images (Ai, Aii, and Aiii, corresponding to the gray boxes). B, ACSAT-determined ROIs from multiple iterations overlying on the time-collapsed image (red, yellow, green, and blue outline corresponds to iteration 1, 2, 3, and 4, respectively). The second (yellow), third (green), and fourth (blue) iterations are shown for comparison although ACSAT terminated at iteration 1 (red).

Fluorescence traces and SNRs. A1, Representative fluorescence traces from the hippocampus dataset A for ROIs identified by both ACSAT and human referees (Match), ROIs identified only by ACSAT, and ROIs identified only by human referees (Human). A2, Histogram of SNR for Match, ACSAT, and human ROIs from the hippocampus dataset A. B1, Representative fluorescence traces from the striatum dataset. B2, Histogram of SNR for the striatum dataset.

Distribution of ROI size for hippocampus dataset A. ROIs identified by ACSAT and human (red) with various size. The ACSAT-only ROIs (yellow) and those missed by human experts (green) tend to have small areas, while the areas of human-only ROIs (blue) appear slightly larger.

Performance of ACSAT over iterations. A, B, For both hippocampus dataset A (A) and striatum dataset (B), the cumulative number of identified ROIs (solid line) increased steadily over iterations. The global threshold (dashed blue line) tended to decrease with each iteration, allowing ACSAT to capture ROIs with lower intensity. Both recall (solid red line) and false discovery rate = 1 – precision (dotted red line) increased with iterations, while the false-negative rate (dashed red line) decreased. All results reported here are based on human-generated ROIs before secondary manual inspection of false positives.

Convergence of the FIBAT optimal global threshold value for the hippocampus dataset A. FIBAT first sampled at a coarse scale across a wide intensity range, and then focused on a small potential intensity range with a fine scale to identify the optimal global threshold value that generated most ROIs. The vertical lines indicate the final optimal global threshold value determined by FIBAT for each iteration.

Improved ROI identification by local thresholding. A, With global thresholding alone, a cluster of hippocampal neurons was identified as a single ROI. B, After application of local thresholding, ACSAT successfully separated five new ROIs from the single ROI. C, Zoom-in of each ROI separated by local thresholding.

Local thresholding improves ACSAT performance for hippocampus dataset A. The ROIs identified by ACSAT at each iteration before local thresholding (left) and after (right). Local thresholding separated overlapping ROIs and thus helped identify more ROIs, including those identified by human (black) or missed by human (red).