{"doi":"10.1002/mrm.28908","title":"Image‐ versus histogram‐based considerations in semantic segmentation of pulmonary hyperpolarized gas images","abstract":"PURPOSE: To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images. METHODS: Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. RESULTS: Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network. CONCLUSIONS: Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.","journal":"Magnetic Resonance in Medicine","year":2021,"id":180445,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9575,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":474492,"name":"Talissa A. Altes","orcid":"0000-0002-8969-8673","position":1,"is_corresponding":false},{"id":479350,"name":"Kun Qing","orcid":"0000-0001-5784-291X","position":2,"is_corresponding":false},{"id":479351,"name":"Mu He","orcid":"0000-0002-7141-6359","position":3,"is_corresponding":false},{"id":323798,"name":"G. Wilson Miller","orcid":"0000-0002-4833-8321","position":4,"is_corresponding":false},{"id":552695,"name":"Brian Avants","orcid":"0000-0002-4212-3362","position":5,"is_corresponding":false},{"id":432924,"name":"Yun M. Shim","orcid":"0000-0002-3780-3371","position":6,"is_corresponding":false},{"id":259733,"name":"James C. Gee","orcid":"0000-0002-2258-0187","position":7,"is_corresponding":false},{"id":474491,"name":"John P. Mugler","orcid":"0000-0002-4140-308X","position":8,"is_corresponding":false},{"id":474493,"name":"Jaime F. Mata","orcid":"0000-0002-2274-1470","position":9,"is_corresponding":false},{"id":233665,"name":"Nicholas J. Tustison","orcid":"0000-0001-9418-5103","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":null,"created_at":"2026-07-18T23:47:57.268805Z","pmid":"34227163","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":[]}