{"doi":"10.1117/1.nph.12.4.045007","title":"Accelerating myelin defect detection in neurodegenerative disorders: a human-in-the-loop deep learning approach with birefringence microscopy","abstract":"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: 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. Conclusions: This approach effectively streamlines myelin defect detection and can enable the scale up of myelin degradation studies in neurodegenerative disorders.","journal":"Neurophotonics","year":2025,"id":580001,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9588,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1479928,"name":"Arjun Chandra","orcid":null,"position":1,"is_corresponding":false},{"id":1391971,"name":"Alexander J. Gray","orcid":"0000-0003-2536-1315","position":2,"is_corresponding":false},{"id":1404406,"name":"Shuying Li","orcid":"0000-0002-0069-6971","position":3,"is_corresponding":false},{"id":1490840,"name":"Mikayla Bradsby","orcid":null,"position":4,"is_corresponding":false},{"id":744565,"name":"Irving J. Bigio","orcid":"0000-0003-4168-1466","position":5,"is_corresponding":false},{"id":1087852,"name":"Anna Novoseltseva","orcid":"0000-0002-2798-6573","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:58:34.718602Z","pmid":"41195092","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}