Non-linear dendritic input integration can greatly boost the computational power of individual neurons. Investigating dendritic population dynamics in regions with sparsely active neurons such as hippocampus using in vivo 2-photon requires calcium imaging of densely labeled preparations. However, current analysis methods face limitations in processing densely labeled dendritic preparations because identification of regions of interest (ROIs) corresponding to individual dendrites and extracting temporal components from overlapping ROIs is difficult. Here we present a method for automatic identification of dendritic ROIs in dense samples, as well as a measure to validate significant calcium transients in detected ROIs. Sparse constrained non-negative matrix factorization (sCNMF) has been used to identify and de-mix dendritic ROIs in dense fields of view, but this requires random initialization and the results can be difficult to map to individual neurons. We modified sCNMF to initialize with regions of contiguous simultaneously active pixels and maintain contiguous ROIs throughout the procedure. This results in deterministic ROIs that are traceable to the parent soma, with efficient time and memory requirements. Given a complete set of ROIs, CNMF extracts accurate temporal signals from overlapping segments. But complete labeling is not guaranteed in dense datasets, which can lead to misattributed activity and corrupted tuning properties. To address this, we define a “fitness trace” of an ROI, reflecting the completeness of ROI activation for each frame. Using this, we automatically identify and discard false calcium transients from overlapping ROIs, saving hours of manual proof-reading time. We integrate these methods into existing imaging pipelines such as CaImAn, automating easy and robust processing of dendritic data in an accessible, open-source manner. We are optimistic that this will pave the way for more studies of dendritic function using increasingly dense fields of view, which will accelerate the field’s understanding of the importance and utility of dendritic activity.