FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362692774> ?p ?o ?g. }

• W4362692774 abstract "Background Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients. Purpose Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients. Study Type Retrospective study. Subjects Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 ± 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section). Field Strength/Sequence A 1.5 T. Sequences: 2D T 1 ‐weighted fast spin echo. Fat fraction (FF): three‐point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT‐prepared GRE, T2: 2D multispin‐echo sequence. Assessment U‐Net 2D, U‐Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10‐fold cross‐validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume). Statistical Tests Bland–Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20−). Results The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0.91 ± 0.08, and measurement biases reaching −0.32% ± 0.92% for FF, 0.19 ± 0.77 for MTR, −0.55 ± 1.95 msec for T2, and − 0.38 ± 3.67 cm 3 for volume. The performances of HRNet, between G20− and G20+ decreased significantly. Data Conclusion HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%). Evidence Level 4. Technical Efficacy Stage 1." @default.
• W4362692774 created "2023-04-09" @default.
• W4362692774 creator A5021955373 @default.
• W4362692774 creator A5023716345 @default.
• W4362692774 creator A5035069302 @default.
• W4362692774 creator A5039825764 @default.
• W4362692774 creator A5055156038 @default.
• W4362692774 creator A5067543873 @default.
• W4362692774 creator A5073873761 @default.
• W4362692774 creator A5086443939 @default.
• W4362692774 creator A5091400465 @default.
• W4362692774 date "2023-04-06" @default.
• W4362692774 modified "2023-10-01" @default.
• W4362692774 title "The Impact of Fatty Infiltration on <scp>MRI</scp> Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches" @default.
• W4362692774 cites W1573164764 @default.
• W4362692774 cites W1901129140 @default.
• W4362692774 cites W2013901689 @default.
• W4362692774 cites W2018722071 @default.
• W4362692774 cites W2034024153 @default.
• W4362692774 cites W2109889650 @default.
• W4362692774 cites W2110363356 @default.
• W4362692774 cites W2127569793 @default.
• W4362692774 cites W2147305697 @default.
• W4362692774 cites W2189787157 @default.
• W4362692774 cites W2248797975 @default.
• W4362692774 cites W2294322619 @default.
• W4362692774 cites W2472992896 @default.
• W4362692774 cites W2589961866 @default.
• W4362692774 cites W2597670594 @default.
• W4362692774 cites W2754599739 @default.
• W4362692774 cites W2757111439 @default.
• W4362692774 cites W2780449831 @default.
• W4362692774 cites W2909024729 @default.
• W4362692774 cites W2946352801 @default.
• W4362692774 cites W2987132611 @default.
• W4362692774 cites W2993751684 @default.
• W4362692774 cites W3000563893 @default.
• W4362692774 cites W3001881024 @default.
• W4362692774 cites W3011319675 @default.
• W4362692774 cites W3021150555 @default.
• W4362692774 cites W3081605114 @default.
• W4362692774 cites W3112931158 @default.
• W4362692774 cites W3123671113 @default.
• W4362692774 cites W3151164742 @default.
• W4362692774 cites W3151361294 @default.
• W4362692774 cites W3200007511 @default.
• W4362692774 cites W4233334050 @default.
• W4362692774 doi "https://doi.org/10.1002/jmri.28708" @default.
• W4362692774 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37025028" @default.
• W4362692774 hasPublicationYear "2023" @default.
• W4362692774 type Work @default.
• W4362692774 citedByCount "1" @default.
• W4362692774 countsByYear W43626927742023 @default.
• W4362692774 crossrefType "journal-article" @default.
• W4362692774 hasAuthorship W4362692774A5021955373 @default.
• W4362692774 hasAuthorship W4362692774A5023716345 @default.
• W4362692774 hasAuthorship W4362692774A5035069302 @default.
• W4362692774 hasAuthorship W4362692774A5039825764 @default.
• W4362692774 hasAuthorship W4362692774A5055156038 @default.
• W4362692774 hasAuthorship W4362692774A5067543873 @default.
• W4362692774 hasAuthorship W4362692774A5073873761 @default.
• W4362692774 hasAuthorship W4362692774A5086443939 @default.
• W4362692774 hasAuthorship W4362692774A5091400465 @default.
• W4362692774 hasBestOaLocation W43626927741 @default.
• W4362692774 hasConcept C105702510 @default.
• W4362692774 hasConcept C124504099 @default.
• W4362692774 hasConcept C126322002 @default.
• W4362692774 hasConcept C126838900 @default.
• W4362692774 hasConcept C143409427 @default.
• W4362692774 hasConcept C154945302 @default.
• W4362692774 hasConcept C163892561 @default.
• W4362692774 hasConcept C2776356578 @default.
• W4362692774 hasConcept C2779018429 @default.
• W4362692774 hasConcept C2779134260 @default.
• W4362692774 hasConcept C2989005 @default.
• W4362692774 hasConcept C41008148 @default.
• W4362692774 hasConcept C71924100 @default.
• W4362692774 hasConcept C81363708 @default.
• W4362692774 hasConcept C89600930 @default.
• W4362692774 hasConceptScore W4362692774C105702510 @default.
• W4362692774 hasConceptScore W4362692774C124504099 @default.
• W4362692774 hasConceptScore W4362692774C126322002 @default.
• W4362692774 hasConceptScore W4362692774C126838900 @default.
• W4362692774 hasConceptScore W4362692774C143409427 @default.
• W4362692774 hasConceptScore W4362692774C154945302 @default.
• W4362692774 hasConceptScore W4362692774C163892561 @default.
• W4362692774 hasConceptScore W4362692774C2776356578 @default.
• W4362692774 hasConceptScore W4362692774C2779018429 @default.
• W4362692774 hasConceptScore W4362692774C2779134260 @default.
• W4362692774 hasConceptScore W4362692774C2989005 @default.
• W4362692774 hasConceptScore W4362692774C41008148 @default.
• W4362692774 hasConceptScore W4362692774C71924100 @default.
• W4362692774 hasConceptScore W4362692774C81363708 @default.
• W4362692774 hasConceptScore W4362692774C89600930 @default.
• W4362692774 hasLocation W43626927741 @default.
• W4362692774 hasLocation W43626927742 @default.
• W4362692774 hasLocation W43626927743 @default.
• W4362692774 hasOpenAccess W4362692774 @default.
• W4362692774 hasPrimaryLocation W43626927741 @default.
• W4362692774 hasRelatedWork W2769435486 @default.

• next