Accelerating myelin defect detection in neurodegenerative disorders: a human-in-the-loop deep learning approach with...

Myelin degradation is a critical yet understudied pathological feature in neurodegenerative disorders. Manual detection of myelin defects in volumetric microscopy images is prohibitively time-consuming, limiting large-scale studies. There is a need for rapid, accurate, and scalable defect-detection methods to accelerate advances in the field.

We aim to develop and evaluate a human-in-the-loop deep learning approach to accelerate myelin defect detection.

We imaged brain tissue samples from the dorsolateral prefrontal cortex from 15 subjects (i.e., five controls, five Alzheimer's disease, and five chronic traumatic encephalopathy) using RGB circular crossed-polarized birefringence microscopy. We created a dataset of 5600 manually annotated myelin defects and trained a YOLOv8-based defect detection model with iterative expert verification.

Our approach achieved 0.85 mAP@50 and reduced analysis time from 8 h to 33 min per $1{\mathrm{mm}}^2$ of tissue while maintaining high accuracy for disease comparison studies. The method can process complete 3D volumetric images up to 300 GB, enabling comprehensive assessment across large tissue volumes.

This approach effectively streamlines myelin defect detection and can enable the scale up of myelin degradation studies in neurodegenerative disorders.