CALHippo: Cell Segmentation for Neuronal Density Inference in the Human Hippocampus

Reliable estimates of cellular composition and anatomical distribution in the human brain are essential for biologically plausible circuit models. In the hippocampus, existing reconstructions rely on low-resolution (LR) data without explicit cell-type-resolved annotations, limiting quantitative maps of excitatory neurons, inhibitory interneurons, and glial cells. Using newly released 1 um/px BigBrain sections of the right hippocampus, we present CALHippo, Cellular Annotation Library for the Hippocampus, a multiscale resource for cell-type-resolved reconstruction of the human CA complex. CALHippo includes the first expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1-CA4) subfields with explicit three-class labels, together with a lower-resolution mesoscale cellular point-cloud map. High-resolution (HR) cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, and are classified as excitatory neurons, inhibitory interneurons, or glial cells. To extend sparse HR annotations to the full volume, we project them into the 20 um/px LR BigBrain space and use the resulting class-specific supervision maps to train a UNet-based density estimation model. The predicted density maps enable slice-by-slice inference across the full CA complex and are sampled to generate a class-resolved mesoscale cellular point cloud. Code (https://github.com/AImageLab-zip/CALHippo-Framework) and dataset (https://ditto.ing.unimore.it/calhippo) are publicly released to support reproducibility.