{"doi":"10.3389/fnins.2020.568614","title":"Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net","abstract":"Accurate removal of magnetic resonance imaging (MRI) signal outside the brain, a.k.a., skull stripping, is a key step in the image pre-processing pipeline. In rodents, this is mostly achieved by manually editing a brain mask, which is time-consuming and operator dependent. Automating this step is particularly challenging in rodents as compared to humans because of differences in brain/scalp tissue geometry, image resolution with respect to brain-scalp distance, and tissue contrast around the skull. In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images. The U-Net method is robust against inter-subject variability and eliminates operator dependence. To benchmark the efficiency of this method, we trained and validated our model using in-house collected and publicly available datasets. In comparison to current state-of-the-art methods, our approach achieved averaged Dice similarity coefficients of 0.97 on T2-weighted anatomical images, and 0.96 on T2*-weighted echo planar imaging, demonstrating robust performance of our approach across various MRI protocols.","journal":"Frontiers in Neuroscience","year":2020,"id":60065,"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":64,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9542,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":289666,"name":"Shuai Wang","orcid":"0000-0003-3730-6401","position":1,"is_corresponding":false},{"id":318934,"name":"Paridhi Ranadive","orcid":null,"position":2,"is_corresponding":false},{"id":318935,"name":"Woomi Ban","orcid":null,"position":3,"is_corresponding":false},{"id":316833,"name":"Tzu-Hao Harry Chao","orcid":"0000-0002-0057-6849","position":4,"is_corresponding":false},{"id":276866,"name":"Sheng Song","orcid":"0000-0001-5676-0533","position":5,"is_corresponding":false},{"id":271348,"name":"Domenic H. Cerri","orcid":"0000-0003-4322-5827","position":6,"is_corresponding":false},{"id":316834,"name":"Lindsay R. Walton","orcid":"0000-0002-9650-9607","position":7,"is_corresponding":false},{"id":316835,"name":"Margaret Broadwater","orcid":"0000-0002-3956-145X","position":8,"is_corresponding":false},{"id":318936,"name":"Sung-Ho Lee","orcid":null,"position":9,"is_corresponding":false},{"id":270449,"name":"Dinggang Shen","orcid":"0000-0002-7934-5698","position":10,"is_corresponding":false},{"id":318937,"name":"Yen-Yu I. Shih","orcid":null,"position":11,"is_corresponding":false},{"id":318933,"name":"Li-Ming Hsu","orcid":null,"position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":null,"created_at":"2026-07-18T21:08:47.852401Z","pmid":"33117118","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":[]}