SpecSeg is a versatile toolbox that segments neurons and neurites in chronic calcium imaging datasets based on low-frequency cross-spectral power.

Imaging calcium signals in neurons of animals using single- or multi-photon microscopy facilitates the study of coding in large neural populations. Such experiments produce massive datasets requiring powerful methods to extract responses from hundreds of neurons. We present SpecSeg, an open-source toolbox for (1) segmentation of regions of interest (ROIs) representing neuronal structures, (2) inspection and manual editing of ROIs, (3) neuropil correction and signal extraction, and (4) matching of ROIs in sequential recordings. ROI segmentation in SpecSeg is based on temporal cross-correlations of low-frequency components derived by Fourier analysis of each pixel with its neighbors. The approach is user-friendly, intuitive, and insightful and enables ROI detection around neurons or neurites. It works for single- (miniscope) and multi-photon microscopy data, eliminating the need for separate toolboxes. SpecSeg thus provides an efficient and versatile approach for analyzing calcium responses in neuronal structures imaged over prolonged periods of time.