(a) Illustration of the parallelization approach used by CaImAn batch for source extraction. The data movie is partitioned into overlapping sub-tensors, each of which is processed in an embarrassingly parallel fashion using CNMF, either on local cores or across several machines in a HPC. The results are then combined. (b) Refinement after combining the results can also be parallelized both in space and in time. Temporal traces of spatially non-overlapping components can be updated in parallel (top) and the contribution of the spatial footprints for each pixel can be computed in parallel (bottom). Parallelization in combination with memory mapping enable large scale processing with moderate computing infrastructure. (c) Quality assessment in space: The spatial footprint of each real component is correlated with the data averaged over time, after removal of all other activity. (d) Quality assessment in time: A high SNR is typically maintained over the course of a calcium transient. (e) CNN based assessment. Top: A 4-layer CNN based classifier is used to classify the spatial footprint of each component into neurons or not, see Materials and methods (Classification through CNNs) for a description. Bottom: Positive and negative examples for the CNN classifier, during training (left) and evaluation (right) phase. The CNN classifier can accurately classify shapes and generalizes across datasets from different brain areas.

(a) Comparison of CaImAn batch (top) and CaImAn online (bottom) when benchmarked against consensus annotation for dataset K53. For a portion of the FOV, correlation image overlaid with matches (left panels, red: consensus, yellow: CaImAn) and mismatches (right panels, red: false negatives, yellow: false positives). (b) Performance of CaImAn batch, and CaImAn online vs average human performance (blue). For each algorithm the results with both the same parameters for each dataset and with the optimized per dataset parameters are shown. CaImAn batch and CaImAn online reach near-human accuracy for neuron detection. Complete results with precision and recall for each dataset are given in Table 1. (c–e) Performance of CaImAn batch increases with peak SNR. (c) Example of scatter plot between SNRs of matched traces between CaImAnbatch and consensus annotation for dataset K53. False negative/positive pairs are plotted in green along the x- and y-axes respectively, perturbed as a point cloud to illustrate the density. Most false positive/negative predictions occur at low SNR values. Shaded areas represent thresholds above which components are considered for matching (blue for CaImAn batch and red for consensus selected components) (d) F1 score and upper/lower bounds of CaImAn batch for all datasets as a function of various peak SNR thresholds. Performance of CaImAn batch increases significantly for neurons with high peak SNR traces (see text for definition of metrics and the bounds). (e) Precision and recall of CaImAn batch as a function of peak SNR for all datasets. The same trend is observed for both precision and recall.

(a) Correlation image overlaid with the spatial components (in red) found by the algorithm (portion of plane 11 out of 45 planes in total). (b) Correlation image (left) and mean image (right) for the dashed region in panel (a) with superimposed the contours of the neurons marked in (a). (c) Spatial (left) and temporal (right) components associated to the ten example neurons marked in panel (a). (d) Temporal traces for all the neurons found in the FOV in (a); the initialization on the first 200 frames contained 500 neurons (present since time 0). (e) Number of neurons found per plane (See also Figure 6—figure supplement 1 for a summary of the results from all planes).

(a) Contour plots of all neurons detected by the CNMF-E (white) implementation of Zhou et al. (2018) and CaImAn batch (red) using patches. Colors match the example traces shown in (b), which illustrate the temporal components of 10 example neurons detected by both implementations CaImAn batch . reproduces with reasonable fidelity the results of Zhou et al. (2018).

(a) Contour plots of neurons that were detected to be active in all six imaging sessions overlaid on the correlation image of the sixth imaging session. Each color corresponds to a different session. (b) Stability of multiday registration method. Comparisons of forward and backward registrations in terms of F1 scores for all possible subsets of sessions. The comparisons agree to a very high level, indicating the stability of the proposed approach. (c) Comparison (in terms of F1 score) of pair-wise alignments using readouts from the union vs direct alignment. The comparison is performed for both the forward and the backwards alignment. For all pairs of sessions the alignment using the proposed method gives very similar results compared to direct pairwise alignment. (d) Magnified version of the tracked neurons corresponding to the squares marked in panel (a). Neurons in different parts of the FOV exhibit different shift patterns over the course of multiple days, but can nevertheless be tracked accurately by the proposed multiday registration method.

(a) Correlation image can efficiently display active neurons. Comparison of median across time (top) and max-correlation (bottom) image for annotated datasets J115 (left), K53 (middle) and YST (right). In all cases, the correlation image aids in manual annotation by providing an efficient way to remove neuropil contamination and visualize the footprints of active neurons. (b) Contour plots of manual annotations (left) vs spatial footprints obtained after running SeededInitialization (right), for dataset J115 overlaid against the mean image. Manual annotations are restricted to be of ellipsoid shape whereas pre-processing with SeededInitialization allows the spatial footprints to adapt to the footprint of each neuron in the FOV. (c) Thresholding of spatial footprints selects the most prominent part of each neuron for comparison against ground truth. Left. Four examples of non thresholded components overlaid to their corresponding contours. Right. Same as left, but including all neurons within a small region. Finding an optimal threshold to generate consistent binary masks can be challenging.

(a) Contour plots of selected components against consensus annotation (CA) for (left) and Suite2p with the use of a classifier (middle) and direct comparison between the algorithms (right) for the test dataset N.00.00. identifies better components with a weak footprint in the summary correlation image. (b) Performance metrics F1 score (top), precision (middle) and recall (bottom), for Suite2p (with and the without the use of the classifier) and for the eight test datasets. consistently outperforms Suite2p, which can have significant variations between precision and recall. See Materials and methods (Comparison with Suite2p) for more details on the comparison.

Profile of spatial (left) and temporal (right) components found in each plane of the whole brain zebrafish recording. (Left) Components are extracted with CaImAn online and then max-thresholded. (Right) See Results section for a complete discussion. .