{"doi":"10.1002/mp.15983","title":"Fully automated segmentation of prostatic urethra for MR‐guided radiation therapy","abstract":"PURPOSE: Accurate delineation of the urethra is a prerequisite for urethral dose reduction in prostate radiotherapy. However, even in magnetic resonance-guided radiation therapy (MRgRT), consistent delineation of the urethra is challenging, particularly in online adaptive radiotherapy. This paper presented a fully automatic MRgRT-based prostatic urethra segmentation framework. METHODS: Twenty-eight prostate cancer patients were included in this study. In-house 3D half fourier single-shot turbo spin-echo (HASTE) and turbo spin echo (TSE) sequences were used to image the Foley-free urethra on a 0.35 T MRgRT system. The segmentation pipeline uses 3D nnU-Net as the base and innovatively combines ground truth and its corresponding radial distance (RD) map during training supervision. Additionally, we evaluate the benefit of incorporating a convolutional long short term memory (LSTM-Conv) layer and spatial recurrent convolution layer (RCL) into nnU-Net. A novel slice-by-slice simple exponential smoothing (SEPS) method specifically for tubular structures was used to post-process the segmentation results. RESULTS: The experimental results show that nnU-Net trained using a combination of Dice, cross-entropy and RD achieved a Dice score of 77.1 ± 2.3% in the testing dataset. With SEPS, Hausdorff distance (HD) and 95% HD were reduced to 2.95 ± 0.17 mm and 1.84 ± 0.11 mm, respectively. LSTM-Conv and RCL layers only minimally improved the segmentation precision. CONCLUSION: We present the first Foley-free MRgRT-based automated urethra segmentation study. Our method is built on a data-driven neural network with novel cost functions and a post-processing step designed for tubular structures. The performance is consistent with the need for online and offline urethra dose reduction in prostate radiotherapy.","journal":"Medical Physics","year":2022,"id":271132,"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":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8714,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"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":858803,"name":"Ting Martin","orcid":"0000-0002-0053-7874","position":1,"is_corresponding":false},{"id":574546,"name":"Ricky R. Savjani","orcid":"0000-0003-4763-2796","position":2,"is_corresponding":false},{"id":935966,"name":"Jonathan Pham","orcid":"0000-0002-5676-6561","position":3,"is_corresponding":false},{"id":234978,"name":"Minsong Cao","orcid":"0000-0003-4705-6629","position":4,"is_corresponding":false},{"id":935967,"name":"Yingli Yang","orcid":"0000-0002-6472-5567","position":5,"is_corresponding":false},{"id":234982,"name":"Amar U. Kishan","orcid":"0000-0002-4836-8483","position":6,"is_corresponding":false},{"id":683237,"name":"Fabien Scalzo","orcid":"0000-0001-9755-8104","position":7,"is_corresponding":false},{"id":347398,"name":"Ke Sheng","orcid":"0000-0002-6696-5409","position":8,"is_corresponding":false},{"id":935965,"name":"Di Xu","orcid":"0000-0001-5587-5275","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":null,"created_at":"2026-07-19T00:27:35.206187Z","pmid":"36106703","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":[]}