In vivo calcium imaging using a 1-photon-based miniscope and a microendoscopic lens enables studies of neural activities in freely behaving animals. However, the high and fluctuating background, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals emitted from imaged neurons inherent in this 1-photon imaging method present major challenges for extracting neuronal signals reliably and automatically from the raw imaging data. Here, we develop a unifying algorithm called the miniscope 1-photon imaging pipeline (MIN1PIPE), which contains several stand-alone modules and can handle a wide range of imaging conditions and qualities with minimal parameter tuning and automatically and accurately isolate spatially localized neural signals. We have quantitatively compared MIN1PIPE with other existing partial methods using both synthetic and real datasets obtained from different animal models and show that MIN1PIPE has superior efficiency and precision in analyzing noisy miniscope calcium imaging data.