{"doi":"10.17615/4fkq-xe49","title":"CENTS: Cortical enhanced neonatal tissue segmentation","abstract":"The acquisition of high-quality magnetic resonance (MR) images of neonatal brains is largely hampered by their characteristically small head size and insufficient tissue contrast. As a result, subsequent image processing and analysis, especially brain tissue segmentation, are often affected. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by augmenting signal-to-noise ratio and spatial resolution without lengthening data acquisition time. In addition, a specialized hybrid atlas-based tissue segmentation algorithm is developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in the to-be-segmented neonatal image with a Hessian filter for generation of a cortical GM confidence map. A neonatal population atlas is then generated by averaging the presegmented images of a population, weighted by their cortical GM similarity with respect to the to-be-segmented image. Finally, the neonatal population atlas is combined with the GM confidence map, and the resulting enhanced tissue probability maps for each tissue form a hybrid atlas that is used for atlas-based segmentation. Various experiments are conducted to compare the segmentations of the proposed method with manual segmentation (on both images acquired with a dedicated phased array coil and a conventional volume coil), as well as with the segmentations of two population-atlas-based methods. Results show the proposed method is capable of segmenting the neonatal brain with the best accuracy, and also preserving the most structural details in the cortical regions.","journal":"UNC Libraries","year":2020,"id":115416,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"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":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":241418,"name":"Lawrence L. Wald","orcid":"0000-0001-8278-6307","position":1,"is_corresponding":false},{"id":314308,"name":"Weili Lin","orcid":"0000-0002-2900-8204","position":2,"is_corresponding":false},{"id":289667,"name":"Pew‐Thian Yap","orcid":"0000-0003-1489-2102","position":3,"is_corresponding":false},{"id":540995,"name":"Jie‐Zhi Cheng","orcid":"0000-0003-3446-8173","position":4,"is_corresponding":false},{"id":537972,"name":"Yong Fan","orcid":"0000-0002-0717-894X","position":5,"is_corresponding":false},{"id":385161,"name":"Feng Shi","orcid":"0000-0003-1522-9943","position":6,"is_corresponding":false},{"id":314179,"name":"John H. Gilmore","orcid":"0000-0002-0939-6764","position":7,"is_corresponding":false},{"id":270449,"name":"Dinggang Shen","orcid":"0000-0002-7934-5698","position":8,"is_corresponding":false},{"id":274286,"name":"Hongyu An","orcid":"0000-0001-6459-2269","position":9,"is_corresponding":false},{"id":399579,"name":"Guido Gerig","orcid":"0000-0002-9547-6233","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:13:36.820928Z","pmid":null,"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":[]}