CalciSeg: A versatile approach for unsupervised segmentation of calcium imaging data

Recent advances in calcium imaging, including the development of fast and sensitive genetically encoded indicators, high-resolution camera chips for wide-field imaging, and resonant scanning mirrors in laser scanning microscopy, have notably improved the temporal and spatial resolution of functional imaging analysis. Nonetheless, the variability of imaging approaches and brain structures challenges the development of versatile and reliable segmentation methods. Standard techniques, such as manual selection of regions of interest or machine learning solutions, often fall short due to either user bias, non-transferability among systems, or computational demand. To overcome these issues, we developed CalciSeg, a data-driven and reproducible approach for unsupervised functional calcium imaging data segmentation. CalciSeg addresses the challenges associated with brain structure variability and user bias by offering a computationally efficient solution for automatic image segmentation based on two parameters: regions' size limits and number of refinement iterations. We evaluated CalciSeg efficacy on datasets of varied complexity, different insect species (locusts, bees, and cockroaches), and imaging systems (wide-field, confocal, and multiphoton), showing the robustness and generality of our approach. Finally, the user-friendly nature and open-source availability of CalciSeg facilitate the integration of this algorithm into existing analysis pipelines.