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

Significance: 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. Aim: We aim to develop and evaluate a human-in-the-loop deep learning approach to accelerate myelin defect detection. Approach: 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. Results: Our approach achieved 0.85 mAP@50 and reduced analysis time from 8 h to 33 min per Conclusions: This approach effectively streamlines myelin defect detection and can enable the scale up of myelin degradation studies in neurodegenerative disorders.