A Deep Learning Pipeline for Comparing Microglia Morphology in Centenarians and Alzheimer’s Disease patients
C. Charlesworth (TU Delft - Electrical Engineering, Mathematics and Computer Science)
X. Zhang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Maruelle C. Luimes – Mentor (Vrije Universiteit Amsterdam)
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Abstract
This thesis investigates differences in microglia morphology between cognitively healthy centenarians and Alzheimer’s disease (AD) patients to better understand mechanisms of cognitive resilience and neurodegeneration. An end-to-end machine learning pipeline was developed for automated microglia detection, segmentation, morphological feature extraction, clustering, and statistical analysis. Key innovations include cross-species transfer learning, morphology-based active learning, topology-aware segmentation, morphology-centric evaluation metrics, and soft clustering of microglial states. By analyzing over one million cells from postmortem brain tissue, the study aims to identify morphology patterns associated with AD pathology and exceptional longevity, providing new insights into the role of microglia in neurodegeneration and healthy cognitive aging.