{"doi":"10.1002/mp.14755","title":"Automated contour propagation of the prostate from pCT to CBCT images via deep unsupervised learning","abstract":"PURPOSE: To develop and evaluate a deep unsupervised learning (DUL) framework based on a regional deformable model for automated prostate contour propagation from planning computed tomography (pCT) to cone-beam CT (CBCT). METHODS: We introduce a DUL model to map the prostate contour from pCT to on-treatment CBCT. The DUL framework used a regional deformable model via narrow-band mapping to augment the conventional strategy. Two hundred and fifty-one anonymized CBCT images from prostate cancer patients were retrospectively selected and divided into three sets: 180 were used for training, 12 for validation, and 59 for testing. The testing dataset was divided into two groups. Group 1 contained 50 CBCT volumes, with one physician-generated prostate contour on CBCT image. Group 2 contained nine CBCT images, each including prostate contours delineated by four independent physicians and a consensus contour generated using the STAPLE method. Results were compared between the proposed DUL and physician-generated contours through the Dice similarity coefficients (DSCs), the Hausdorff distances, and the distances of the center-of-mass. RESULTS: The average DSCs between DUL-based prostate contours and reference contours for test data in group 1 and group 2 consensus were 0.83 ± 0.04, and 0.85 ± 0.04, respectively. Correspondingly, the mean center-of-mass distances were 3.52 mm ± 1.15 mm, and 2.98 mm ± 1.42 mm, respectively. CONCLUSIONS: This novel DUL technique can automatically propagate the contour of the prostate from pCT to CBCT. The proposed method shows that highly accurate contour propagation for CBCT-guided adaptive radiotherapy is achievable via the deep learning technique.","journal":"Medical Physics","year":2021,"id":173138,"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":24,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9555,"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":710222,"name":"Jean‐Emmanuel Bibault","orcid":"0000-0002-1728-6776","position":1,"is_corresponding":false},{"id":710903,"name":"Thomas Leroy","orcid":null,"position":2,"is_corresponding":false},{"id":710223,"name":"Alexandre Escande","orcid":"0000-0002-3603-6190","position":3,"is_corresponding":false},{"id":301438,"name":"Wei Zhao","orcid":"0000-0002-6182-4746","position":4,"is_corresponding":false},{"id":710224,"name":"Yizheng Chen","orcid":"0000-0001-8799-2580","position":5,"is_corresponding":false},{"id":262586,"name":"Mark K. Buyyounouski","orcid":"0000-0001-9294-9421","position":6,"is_corresponding":false},{"id":503122,"name":"Steven Hancock","orcid":"0000-0001-5659-6964","position":7,"is_corresponding":false},{"id":435674,"name":"H.P. Bagshaw","orcid":"0000-0002-5888-4684","position":8,"is_corresponding":false},{"id":258394,"name":"Lei Xing","orcid":"0000-0003-2536-5359","position":9,"is_corresponding":false},{"id":503121,"name":"Xiaokun Liang","orcid":"0000-0002-1207-5726","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-18T23:46:45.768135Z","pmid":"33544390","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":[]}