FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3127243174> ?p ?o ?g. }

• W3127243174 endingPage "e200078" @default.
• W3127243174 startingPage "e200078" @default.
• W3127243174 abstract "Purpose To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Materials and Methods A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results Six teams (T1–T6) submitted entries for the challenge. No differences were observed across any segmentation metrics for any tissues (P = .99) among the four top-performing networks (T2, T3, T4, T6). Dice coefficient correlations between network pairs were high (> 0.85). Per-scan thickness errors were negligible among networks T1–T4 (P = .99), and longitudinal changes showed minimal bias (< 0.03 mm). Low correlations (ρ < 0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top-performing networks (P = .99). Empirical upper-bound performances were similar for both combinations (P = .99). Conclusion Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance. See also the commentary by Elhalawani and Mak in this issue. Keywords: Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020 Supplemental material is available for this article." @default.
• W3127243174 created "2021-02-15" @default.
• W3127243174 creator A5000247754 @default.
• W3127243174 creator A5004619468 @default.
• W3127243174 creator A5006203153 @default.
• W3127243174 creator A5008183217 @default.
• W3127243174 creator A5011588824 @default.
• W3127243174 creator A5012995283 @default.
• W3127243174 creator A5013235362 @default.
• W3127243174 creator A5014387832 @default.
• W3127243174 creator A5017843895 @default.
• W3127243174 creator A5024492906 @default.
• W3127243174 creator A5024882772 @default.
• W3127243174 creator A5030188696 @default.
• W3127243174 creator A5032322712 @default.
• W3127243174 creator A5038369687 @default.
• W3127243174 creator A5042628612 @default.
• W3127243174 creator A5043462798 @default.
• W3127243174 creator A5043691466 @default.
• W3127243174 creator A5044405214 @default.
• W3127243174 creator A5044571053 @default.
• W3127243174 creator A5045030373 @default.
• W3127243174 creator A5045433274 @default.
• W3127243174 creator A5050471723 @default.
• W3127243174 creator A5061138984 @default.
• W3127243174 creator A5062564649 @default.
• W3127243174 creator A5064829377 @default.
• W3127243174 creator A5080030813 @default.
• W3127243174 creator A5085348888 @default.
• W3127243174 creator A5087411245 @default.
• W3127243174 creator A5087872816 @default.
• W3127243174 date "2021-05-01" @default.
• W3127243174 modified "2023-10-17" @default.
• W3127243174 title "The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset" @default.
• W3127243174 cites W1871971828 @default.
• W3127243174 cites W1967057044 @default.
• W3127243174 cites W1978598945 @default.
• W3127243174 cites W2040447069 @default.
• W3127243174 cites W2059975159 @default.
• W3127243174 cites W2101197911 @default.
• W3127243174 cites W2118607036 @default.
• W3127243174 cites W2132697886 @default.
• W3127243174 cites W2140513563 @default.
• W3127243174 cites W2160591877 @default.
• W3127243174 cites W2187638581 @default.
• W3127243174 cites W2614500948 @default.
• W3127243174 cites W2794990008 @default.
• W3127243174 cites W2803537647 @default.
• W3127243174 cites W2959938807 @default.
• W3127243174 cites W2989916101 @default.
• W3127243174 cites W2990697799 @default.
• W3127243174 cites W2992133468 @default.
• W3127243174 cites W3013294478 @default.
• W3127243174 cites W3018859385 @default.
• W3127243174 cites W3080893417 @default.
• W3127243174 cites W3084251109 @default.
• W3127243174 cites W3091992955 @default.
• W3127243174 cites W3100331755 @default.
• W3127243174 cites W3103145119 @default.
• W3127243174 cites W3106061934 @default.
• W3127243174 doi "https://doi.org/10.1148/ryai.2021200078" @default.
• W3127243174 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8231759" @default.
• W3127243174 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34235438" @default.
• W3127243174 hasPublicationYear "2021" @default.
• W3127243174 type Work @default.
• W3127243174 sameAs 3127243174 @default.
• W3127243174 citedByCount "41" @default.
• W3127243174 countsByYear W31272431742020 @default.
• W3127243174 countsByYear W31272431742021 @default.
• W3127243174 countsByYear W31272431742022 @default.
• W3127243174 countsByYear W31272431742023 @default.
• W3127243174 crossrefType "journal-article" @default.
• W3127243174 hasAuthorship W3127243174A5000247754 @default.
• W3127243174 hasAuthorship W3127243174A5004619468 @default.
• W3127243174 hasAuthorship W3127243174A5006203153 @default.
• W3127243174 hasAuthorship W3127243174A5008183217 @default.
• W3127243174 hasAuthorship W3127243174A5011588824 @default.
• W3127243174 hasAuthorship W3127243174A5012995283 @default.
• W3127243174 hasAuthorship W3127243174A5013235362 @default.
• W3127243174 hasAuthorship W3127243174A5014387832 @default.
• W3127243174 hasAuthorship W3127243174A5017843895 @default.
• W3127243174 hasAuthorship W3127243174A5024492906 @default.
• W3127243174 hasAuthorship W3127243174A5024882772 @default.
• W3127243174 hasAuthorship W3127243174A5030188696 @default.
• W3127243174 hasAuthorship W3127243174A5032322712 @default.
• W3127243174 hasAuthorship W3127243174A5038369687 @default.
• W3127243174 hasAuthorship W3127243174A5042628612 @default.
• W3127243174 hasAuthorship W3127243174A5043462798 @default.
• W3127243174 hasAuthorship W3127243174A5043691466 @default.
• W3127243174 hasAuthorship W3127243174A5044405214 @default.
• W3127243174 hasAuthorship W3127243174A5044571053 @default.
• W3127243174 hasAuthorship W3127243174A5045030373 @default.
• W3127243174 hasAuthorship W3127243174A5045433274 @default.
• W3127243174 hasAuthorship W3127243174A5050471723 @default.
• W3127243174 hasAuthorship W3127243174A5061138984 @default.
• W3127243174 hasAuthorship W3127243174A5062564649 @default.
• W3127243174 hasAuthorship W3127243174A5064829377 @default.
• W3127243174 hasAuthorship W3127243174A5080030813 @default.

• next