CALHippo - Mapping neurons and glial cells in the human brain hippocampus in 3D using SOTA segmentation and density estimation models [R]

Overview

Hello everyone! I'm posting our research work as you might be interested in how we used ML to map part of the brain cells of the human hippocampus. We used various human brain slices at high resolution (1 micrometer per pixel) and developed a custom segmentation pipeline that uses SoTA whole slice cell segmentation networks, like CellPoseSAM with good zero shot performances.

Segmentation Pipeline

We refined semi-automatically those annotations and ensembled more finetuned models within the pipeline, adding a merging algorithm and a cell classification for 3 classes:

• Excitatory neurons
• Inhibitory neurons
• Glial cells

Density Estimation

The high-res slices covered only a few parts of the hippocampus with respect to other slices scanned at 20x less the resolution where the cell nuclei are only 1 pixel wide. So we tried to map the high-res annotations we obtained to the low-res corresponding slices, and used a small UNet to supervise a density estimation task for 3 classes. We obtained a network that outputs a density map that can be sampled to obtain a probabilistic map of the cellular positions.

3D Reconstruction

Finally, to reconstruct the volume, we stacked together all the low-resolution density maps from all the slices that covered the hippocampus and obtained a point cloud, which you can see in the GIF along the corresponding anatomical CA (Cornus Ammonis) areas.

Results and Discussion

The performances are still limited by the quantity of data and low-resolution slices, but we showed that the results were biologically plausible given previous estimates by other researchers. The paper was accepted at MICCAI 2026 a few weeks ago! Feedback is very welcome, especially on the density-estimation formulation and possible uses of the generated point cloud.