{"doi":"10.1007/s10334-020-00889-7","title":"Accuracy and longitudinal reproducibility of quantitative femorotibial cartilage measures derived from automated U-Net-based segmentation of two different MRI contrasts: data from the osteoarthritis initiative healthy reference cohort","abstract":"OBJECTIVE: To evaluate the agreement, accuracy, and longitudinal reproducibility of quantitative cartilage morphometry from 2D U-Net-based automated segmentations for 3T coronal fast low angle shot (corFLASH) and sagittal double echo at steady-state (sagDESS) MRI. METHODS: 2D U-Nets were trained using manual, quality-controlled femorotibial cartilage segmentations available for 92 Osteoarthritis Initiative healthy reference cohort participants from both corFLASH and sagDESS (n = 50/21/21 training/validation/test-set). Cartilage morphometry was computed from automated and manual segmentations for knees from the test-set. Agreement and accuracy were evaluated from baseline visits (dice similarity coefficient: DSC, correlation analysis, systematic offset). The longitudinal reproducibility was assessed from year-1 and -2 follow-up visits (root-mean-squared coefficient of variation, RMSCV%). RESULTS: Automated segmentations showed high agreement (DSC 0.89-0.92) and high correlations (r ≥ 0.92) with manual ground truth for both corFLASH and sagDESS and only small systematic offsets (≤ 10.1%). The automated measurements showed a similar test-retest reproducibility over 1 year (RMSCV% 1.0-4.5%) as manual measurements (RMSCV% 0.5-2.5%). DISCUSSION: The 2D U-Net-based automated segmentation method yielded high agreement compared with manual segmentation and also demonstrated high accuracy and longitudinal test-retest reproducibility for morphometric analysis of articular cartilage derived from it, using both corFLASH and sagDESS.","journal":"Magnetic Resonance Materials in Physics Biology and Medicine","year":2020,"id":63231,"datarank":1.4918336903723757,"base_score":3.8501476017100584,"endowment":3.8501476017100584,"self_citation_contribution":0.5775221402565088,"citation_network_contribution":0.9143115501158668,"self_endowment_contribution":0.5775221402565088,"citer_contribution":0.9143115501158668,"corpus_percentile":86.0601841107759,"corpus_rank":1803,"citation_count":46,"citer_count":27,"citers_with_citation_signal":23,"citers_with_endowment":23,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6418,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":325186,"name":"F. Eckstein","orcid":"0000-0002-2014-8278","position":1,"is_corresponding":false},{"id":334415,"name":"Jana Kemnitz","orcid":"0000-0003-0342-4952","position":2,"is_corresponding":false},{"id":334416,"name":"Christian F. Baumgartner","orcid":"0000-0002-3629-4384","position":3,"is_corresponding":false},{"id":334417,"name":"Ender Konukoğlu","orcid":"0000-0002-2542-3611","position":4,"is_corresponding":false},{"id":335706,"name":"David Fuerst","orcid":null,"position":5,"is_corresponding":false},{"id":301558,"name":"Akshay Chaudhari","orcid":"0000-0002-3667-6796","position":6,"is_corresponding":false},{"id":325184,"name":"W. Wirth","orcid":"0000-0002-2297-8283","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-18T21:11:00.632961Z","pmid":"33025284","pmcid":"PMC8154803","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":[]}