CALHippo builds a three-class, CA1–CA4-wide cell annotation library from 1 µm/px BigBrain sections and trains a UNet density model to infer cell density across the CA complex
Using newly released 1 µm/px BigBrain sections of the right hippocampus, this work presents CALHippo, a Cellular Annotation Library for the Hippocampus: an expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1–CA4) subfields with explicit three-class labels for excitatory neurons, inhibitory interneurons, and glial cells, together with a lower-resolution mesoscale cellular point-cloud map; high-resolution cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, then projected into 20 µm/px low-resolution BigBrain space to produce class-specific supervision maps used to train a UNet-based density estimation model, enabling slice-by-slice inference across the ful