{"doi":"10.1162/imag.a.1067","title":"TissUnet: Improved extracranial tissue and cranium segmentation for children through adulthood","abstract":"Extracranial tissues visible on brain magnetic resonance imaging (MRI) may hold significant value for characterizing health conditions and clinical decision-making, yet they are rarely quantified. Current tools have not been widely validated, particularly in settings of developing brains or underlying pathology. We present TissUnet, a deep learning model that segments skull bone, subcutaneous fat, and muscle from routine three-dimensional T1-weighted MRI, with or without contrast enhancement. The model was trained on 155 paired MRI-computed tomography (CT) scans and validated across nine datasets covering a wide age range and including individuals with brain tumors. In comparison to AI-CT-derived labels from 37 MRI-CT pairs, TissUnet achieved a median Dice coefficient of 0.79 [IQR: 0.77-0.81] in a healthy adult cohort. In a second validation using expert manual annotations, median Dice was 0.83 [IQR: 0.83-0.84] in healthy individuals and 0.81 [IQR: 0.78-0.83] in tumor cases, outperforming a previous state-of-the-art method. Acceptability testing resulted in an 89% acceptance rate after adjudication by a tie-breaker (N = 108 MRIs), and TissUnet demonstrated excellent performance in the blinded comparative review (N = 45 MRIs), including both healthy and tumor cases in pediatric populations. TissUnet enables fast, accurate, and reproducible segmentation of extracranial tissues, supporting large-scale studies on craniofacial morphology, treatment effects, and cardiometabolic risk using standard brain T1w MRI.","journal":"Imaging Neuroscience","year":2025,"id":586185,"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":0.9525,"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":1500673,"name":"Elvira Yang","orcid":null,"position":1,"is_corresponding":false},{"id":1072031,"name":"Anna Zapaishchykova","orcid":"0000-0001-6860-9160","position":2,"is_corresponding":false},{"id":1500674,"name":"Yu-Hui Chen","orcid":null,"position":3,"is_corresponding":false},{"id":1500675,"name":"Lucas Heilbroner","orcid":null,"position":4,"is_corresponding":false},{"id":1435482,"name":"John Zielke","orcid":"0000-0002-8655-3508","position":5,"is_corresponding":false},{"id":1166134,"name":"Divyanshu Tak","orcid":"0009-0003-4183-209X","position":6,"is_corresponding":false},{"id":1500676,"name":"Reza Mojahed-Yazdi","orcid":null,"position":7,"is_corresponding":false},{"id":1500677,"name":"Francesca Romana Mussa","orcid":null,"position":8,"is_corresponding":false},{"id":351629,"name":"Zezhong Ye","orcid":"0000-0003-0369-7432","position":9,"is_corresponding":false},{"id":496269,"name":"Sridhar Vajapeyam","orcid":"0000-0002-1782-1919","position":10,"is_corresponding":false},{"id":1072542,"name":"Viviana Benitez","orcid":null,"position":11,"is_corresponding":false},{"id":1471524,"name":"Ralph Salloum","orcid":"0000-0001-9437-0616","position":12,"is_corresponding":false},{"id":1500678,"name":"Susan N. Chi","orcid":null,"position":13,"is_corresponding":false},{"id":1262994,"name":"Houman Sotoudeh","orcid":"0000-0002-5510-7062","position":14,"is_corresponding":false},{"id":52331,"name":"Jakob Seidlitz","orcid":"0000-0002-8164-7476","position":15,"is_corresponding":false},{"id":32673,"name":"Sabine Mueller","orcid":"0000-0002-3452-5150","position":16,"is_corresponding":false},{"id":37161,"name":"Hugo J.W.L. Aerts","orcid":"0000-0002-2122-2003","position":17,"is_corresponding":false},{"id":1500679,"name":"Tina Y. Poussaint","orcid":null,"position":18,"is_corresponding":false},{"id":229101,"name":"Benjamin H. Kann","orcid":"0000-0002-4313-2754","position":19,"is_corresponding":false},{"id":1500672,"name":"Markiian Mandzak","orcid":null,"position":0,"is_corresponding":true}],"reference_count":25,"raw_metadata":null,"created_at":"2026-07-19T02:59:28.666390Z","pmid":"41503005","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":[]}