{"doi":"10.21037/qims-20-1114","title":"MRI classification using semantic random forest with auto-context model","abstract":"BACKGROUND: It is challenging to differentiate air and bone on MR images of conventional sequences due to their low contrast. We propose to combine semantic feature extraction under auto-context manner into random forest to improve reasonability of the MRI segmentation for MRI-based radiotherapy treatment planning or PET attention correction. METHODS: We applied a semantic classification random forest (SCRF) method which consists of a training stage and a segmentation stage. In the training stage, patch-based MRI features were extracted from registered MRI-CT training images, and the most informative elements were selected via feature selection to train an initial random forest. The rest sequence of random forests was trained by a combination of MRI feature and semantic feature under an auto-context manner. During segmentation, the MRI patches were first fed into these random forests to derive patch-based segmentation. By using patch fusion, the final end-to-end segmentation was obtained. RESULTS: The Dice similarity coefficient (DSC) for air, bone and soft tissue classes obtained via proposed method were 0.976±0.007, 0.819±0.050 and 0.932±0.031, compared to 0.916±0.099, 0.673±0.151 and 0.830±0.083 with random forest (RF), and 0.942±0.086, 0.791±0.046 and 0.917±0.033 with U-Net. SCRF also outperformed the competing methods in sensitivity and specificity for all three structure types. CONCLUSIONS: The proposed method accurately segmented bone, air and soft tissue. It is promising in facilitating advanced MR application in diagnosis and therapy.","journal":"Quantitative Imaging in Medicine and Surgery","year":2021,"id":209181,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9651,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"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":236221,"name":"Tonghe Wang","orcid":"0000-0001-9021-1204","position":1,"is_corresponding":false},{"id":796371,"name":"Xue Dong","orcid":"0000-0002-7259-8983","position":2,"is_corresponding":false},{"id":426285,"name":"Sibo Tian","orcid":"0000-0001-5018-4854","position":3,"is_corresponding":false},{"id":245785,"name":"Yingzi Liu","orcid":"0000-0003-1613-900X","position":4,"is_corresponding":false},{"id":289399,"name":"Hui Mao","orcid":"0000-0002-0147-6022","position":5,"is_corresponding":false},{"id":236224,"name":"Walter J. Curran","orcid":"0000-0002-7552-4453","position":6,"is_corresponding":false},{"id":294153,"name":"Hui‐Kuo G. Shu","orcid":"0000-0002-4060-0874","position":7,"is_corresponding":false},{"id":678128,"name":"Tian Liu","orcid":"0000-0001-8944-6305","position":8,"is_corresponding":false},{"id":236226,"name":"Xiaofeng Yang","orcid":"0000-0001-9023-5855","position":9,"is_corresponding":false},{"id":236222,"name":"Yang Lei","orcid":"0000-0002-3572-0345","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-18T23:52:01.526969Z","pmid":"34888187","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":[]}