{"doi":"10.1016/j.intonc.2025.03.003","title":"Assessing quantitative performance and expert review of multiple deep learning-based frameworks for computed tomography-based abdominal organ auto-segmentation","abstract":"Segmentation of abdominal organs in computed tomography (CT) images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up. However, manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability. Many deep learning and automated machine learning (AutoML) frameworks have emerged as a solution to this challenge and show promise in clinical workflows. This study aims to provide a comprehensive evaluation of existing AutoML frameworks (Auto3DSeg, nnU-Net) against a state-of-the-art non-AutoML framework, the Shifted Window U-Net Transformer (SwinUNETR). Each framework was trained on the same 122 training images, taken from the Abdominal Multi-Organ Segmentation (AMOS) Grand Challenge. Frameworks were compared using dice similarity coefficient (DSC), surface DSC (sDSC), and 95th percentile Hausdorff distances (HD95) on an additional 72 holdout-validation images. The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation. Comparisons show significantly better performance by AutoML methods: nnU-Net (average DSC: 0.924, average sDSC: 0.938, average HD95: 4.26, median Likert: 4.57), Auto3DSeg (average DSC: 0.902, average sDSC: 0.919, average HD95: 8.76, median Likert: 4.49), and SwinUNETR (average DSC: 0.837, average sDSC: 0.844, average HD95: 13.93). AutoML frameworks were quantitatively preferred (13/13 organs at risks [OARs] P < 0.05 in DSC and sDSC, 12/13 OARs P < 0.05 in HD95, comparing Auto3DSeg to SwinUNETR, and all OARs P < 0.05 in all metrics comparing SwinUNETR to nnU-Net). Qualitatively, nnU-Net was preferred over Auto3DSeg ( P = 0.0027). The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs, and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.","journal":"Intelligent Oncology","year":2025,"id":539056,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9488,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1091589,"name":"Joel A. Pogue","orcid":"0000-0003-4450-6698","position":1,"is_corresponding":false},{"id":327163,"name":"M. Soike","orcid":"0000-0002-8444-3241","position":2,"is_corresponding":false},{"id":323939,"name":"Neil T. Pfister","orcid":"0000-0001-7369-5710","position":3,"is_corresponding":false},{"id":1425846,"name":"Rojymon Jacob","orcid":"0000-0003-2912-8422","position":4,"is_corresponding":false},{"id":299192,"name":"Carlos Cárdenas","orcid":"0000-0003-1414-3849","position":5,"is_corresponding":false},{"id":1425845,"name":"Udbhav S Ram","orcid":"0000-0002-0457-7259","position":0,"is_corresponding":true}],"reference_count":27,"raw_metadata":null,"created_at":"2026-07-19T02:52:25.799399Z","pmid":"41020282","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":[]}