{"doi":"10.1117/12.2653387","title":"Cascaded neural network segmentation pipeline for automated delineation of prostate and organs at risk in male pelvic CT","abstract":"Delineation of the prostate and nearby organs at risk (OARs) is a fundamental step in prostate cancer radiation therapy planning. Such contouring is often done manually, which can be a time-consuming and highly variable process. To alleviate these issues, we propose a fully automated two-step deep learning approach to segment the prostate, bladder, rectum, seminal vesicles, and femoral heads from CT images. The first step localizes the organs of interest using a modified 3D UNet architecture that contains an axial cross-attention module. Final segmentations are then computed for each organ individually using organ-specifically optimized UNet-based models. A total of 275 CT images were used for model training and validation. When evaluated on a hold-out set of 15 image sets, the full pipeline achieved mean dice similarity coefficients (DSC) and 95% Hausdorff distances (95HD, in mm) of 0.866±0.034 and 4.46±1.02 (prostate), 0.957±0.014 and 2.91±0.289 (bladder), 0.853±0.044 and 5.10±1.87 (rectum), 0.740±0.117 and 6.72±9.46 (seminal vesicles), 0.942±0.016 and 2.85±1.04 (left femoral head), 0.942±0.018 and 3.04±1.37 (right femoral head).","journal":"PubMed","year":2023,"id":398958,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9578,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":356944,"name":"Daniel Y. Song","orcid":"0000-0002-3495-8677","position":1,"is_corresponding":false},{"id":705077,"name":"Junghoon Lee","orcid":"0000-0002-3004-8625","position":2,"is_corresponding":false},{"id":404173,"name":"Rahul Pemmaraju","orcid":"0000-0003-3035-0258","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T01:19:51.724387Z","pmid":"41069862","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":[]}