{"doi":"10.1117/12.3009682","title":"Human microscopic vagus nerve anatomy using deep learning on 3D-MUSE images","abstract":"We are microscopically imaging and analyzing the human vagus nerve (VN) anatomy to create the first ever VN connectome to support modeling of neuromodulation therapies. Although micro-CT and MRI roughly identify vagus nerve anatomy, they lack the spatial resolution required to identify small fascicle splitting and merging, and perineurium boundaries. We developed 3D serial block-face Microscopy with Ultraviolet Surface Excitation (3D-MUSE), with 0.9-μm in-plane resolution and 3-μm cut thickness. 3D-MUSE is ideal for VN imaging, capturing large myelinated fibers, connective sheaths, fascicle dynamics, and nerve bundle tractography. Each 3-mm 3D-MUSE ROI generates ~1,000 grayscale images, necessitating automatic segmentation as over 50-hrs were spent manually annotating fascicles, perineurium, and epineurium in every 20th image, giving 50 images. We trained three types of multi-class deep learning segmentation models. First, 25 annotated images trained a 2D U-Net and Attention U-Net. Second, we trained a Vision Transformer (ViT) using self-supervised learning with 200 unlabeled images before refining the ViT's initialized weights of a U-Net Transformer with 25 training images and labels. Third, we created pseudo-3D images by concatenating each annotated image with an image ±k slices apart (k=1,10), and trained a 2D U-Net similarly. All models were tested on 25 held-out images and evaluated using Dice. While all trained models performed comparably, the 2D U-Net model trained on pseudo-3D images demonstrated highest Dice values (0.936). With sample-based-training, one obtains very promising results on thousands of images in terms of segmentation and nerve fiber tractography estimation. Additional training from more samples could obtain excellent results.","journal":"PubMed","year":2024,"id":490061,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7693,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":311207,"name":"Chaitanya Kolluru","orcid":"0000-0002-3211-7794","position":1,"is_corresponding":false},{"id":479496,"name":"James M. Seckler","orcid":"0000-0003-3895-7257","position":2,"is_corresponding":false},{"id":1335327,"name":"Jun Chen","orcid":"0000-0002-8084-9332","position":3,"is_corresponding":false},{"id":323658,"name":"Justin N. Kim","orcid":"0000-0003-4713-8552","position":4,"is_corresponding":false},{"id":391990,"name":"Michael W. Jenkins","orcid":"0000-0002-8908-5383","position":5,"is_corresponding":false},{"id":1335556,"name":"Andrew Shofstall","orcid":null,"position":6,"is_corresponding":false},{"id":1335557,"name":"Nikki Pelot","orcid":null,"position":7,"is_corresponding":false},{"id":311212,"name":"David L. Wilson","orcid":"0000-0001-9763-1463","position":8,"is_corresponding":false},{"id":1095616,"name":"Naomi Joseph","orcid":null,"position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":null,"created_at":"2026-07-19T02:08:32.775003Z","pmid":"40949787","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":[]}