{"doi":"10.3174/ajnr.a7419","title":"Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach","abstract":"BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed to develop a deep learning-based automatic fetal brain segmentation method that provides improved accuracy and robustness compared with atlas-based methods. MATERIALS AND METHODS: A total of 106 fetal MR imaging studies were acquired prospectively from fetuses between 23 and 39 weeks of gestation. We trained a deep learning model on the MR imaging scans of 65 healthy fetuses and compared its performance with a 4D atlas-based segmentation method using the Wilcoxon signed-rank test. The trained model was also evaluated on data from 41 fetuses diagnosed with congenital heart disease. RESULTS: < .001) based on the Dice score and 95% Hausdorff distance in all brain regions compared with the atlas-based method. The performance of the proposed method was consistent across gestational ages. The segmentations of the brains of fetuses with high-risk congenital heart disease were also highly consistent with the manual segmentation, though the Dice score was 7% lower than that of healthy fetuses. CONCLUSIONS: The proposed deep learning method provides an efficient and reliable approach for fetal brain segmentation, which outperformed segmentation based on a 4D atlas and has been used in clinical and research settings.","journal":"American Journal of Neuroradiology","year":2022,"id":237895,"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":57,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9035,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":465334,"name":"Josepheen De Asis‐Cruz","orcid":"0000-0003-3936-0380","position":1,"is_corresponding":false},{"id":58496,"name":"Xue Feng","orcid":"0000-0002-2181-9889","position":2,"is_corresponding":false},{"id":563039,"name":"Yixuan Wu","orcid":"0000-0003-2541-5133","position":3,"is_corresponding":false},{"id":243156,"name":"Kushal Kapse","orcid":"0000-0001-8363-5404","position":4,"is_corresponding":false},{"id":860868,"name":"A. Largent","orcid":"0000-0003-0336-6350","position":5,"is_corresponding":false},{"id":861395,"name":"J. Quistorff","orcid":null,"position":6,"is_corresponding":false},{"id":396881,"name":"Catherine Lopez","orcid":"0000-0001-6391-3796","position":7,"is_corresponding":false},{"id":437768,"name":"Dan Wu","orcid":"0000-0002-9303-5821","position":8,"is_corresponding":false},{"id":479350,"name":"Kun Qing","orcid":"0000-0001-5784-291X","position":9,"is_corresponding":false},{"id":686350,"name":"Craig H. Meyer","orcid":"0000-0002-7288-3848","position":10,"is_corresponding":false},{"id":243160,"name":"Catherine Limperopoulos","orcid":"0000-0003-1735-0069","position":11,"is_corresponding":false},{"id":243157,"name":"Li Zhao","orcid":"0000-0003-4933-3183","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-19T00:22:21.693802Z","pmid":"35177547","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":[]}