Automated Cell Quantification in Hypoxic-Ischemic Fetal Sheep Brain Histology: A Two-Step Segmentation and...

Hypoxic-ischemic encephalopathy (HIE) is caused by oxygen deprivation to the brain around the time of birth. Therapeutic hypothermia (TH) remains the only validated treatment, and current drug development is hindered by time-consuming manual cell counting and analysis. The field currently lacks robust automated methodologies capable of accurately quantifying cortical and subcortical neuronal cells.This study proposes a two-step cell analysis pipeline for segmentation of neurons and classification of their morphology in fetal sheep brains, with immediate utility in pre-clinical HIE drug discovery. A new dataset containing 180 images and 44,000 cells across 3 treatment groups (including sham, ischemia only, and ischemia treated with hypothermia) from 6 brain regions (CA1, CA3, CA4, DG hippocampal regions and PS1, PS2 parasagittal cortical regions) was manually annotated and used to train a generalized Mask R-CNN, achieving an average precision of 88.3%±1.9% (IoU threshold=0.5). A subsequent dataset (n=1500) of healthy, intermediate, and pyknotic cells was created and used to train a custom, lightweight CNN architecture to classify cells, achieving an overall accuracy of 93.0±0.5% with no healthy-pyknotic misclassifications. The final pipeline outputs novel indicators of HIE-impacted neuronal damage through proportion of healthy neurons. The pipeline's predicted count for number of healthy cells is correlated with original manual quantifications (Spearman's R=0.822, 0.869, 0.919 across treatment groups), validating the model's clinical performance.Clinical relevance - The proposed pipeline can accelerate preclinical drug development for HI in fetal sheep models by minimizing observer bias, reducing labor and time costs, and enabling a more detailed evaluation of neuronal damage.