These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
119 related articles for article (PubMed ID: 29269318)
21. Simultaneous muscle water T2 and fat fraction mapping using transverse relaxometry with stimulated echo compensation. Marty B; Baudin PY; Reyngoudt H; Azzabou N; Araujo EC; Carlier PG; de Sousa PL NMR Biomed; 2016 Apr; 29(4):431-43. PubMed ID: 26814454 [TBL] [Abstract][Full Text] [Related]
22. Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches. Le Troter A; Fouré A; Guye M; Confort-Gouny S; Mattei JP; Gondin J; Salort-Campana E; Bendahan D MAGMA; 2016 Apr; 29(2):245-57. PubMed ID: 26983429 [TBL] [Abstract][Full Text] [Related]
23. Automated segmentation of human cervical-supraclavicular adipose tissue in magnetic resonance images. Lundström E; Strand R; Forslund A; Bergsten P; Weghuber D; Ahlström H; Kullberg J Sci Rep; 2017 Jun; 7(1):3064. PubMed ID: 28596551 [TBL] [Abstract][Full Text] [Related]
24. A Valid and Precise Semiautomated Method for Quantifying Intermuscular Fat Intramuscular Fat in Lower Leg Magnetic Resonance Images. Wong AKO; Szabo E; Erlandson M; Sussman MS; Duggina S; Song A; Reitsma S; Gillick H; Adachi JD; Cheung AM J Clin Densitom; 2020; 23(4):611-622. PubMed ID: 30352783 [TBL] [Abstract][Full Text] [Related]
25. Automated assessment of thigh composition using machine learning for Dixon magnetic resonance images. Yang YX; Chong MS; Tay L; Yew S; Yeo A; Tan CH MAGMA; 2016 Oct; 29(5):723-31. PubMed ID: 27026244 [TBL] [Abstract][Full Text] [Related]
26. The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation. Andrews S; Hamarneh G IEEE Trans Med Imaging; 2015 Sep; 34(9):1773-87. PubMed ID: 25700442 [TBL] [Abstract][Full Text] [Related]
27. [Magnetic resonance imaging of dystrophinopathy that mimics adductor enthesopathy]. Zheng YM; Li WZ; Wang ZX; Zhang W; Lv H; Xiao JX; Yuan Y Beijing Da Xue Xue Bao Yi Xue Ban; 2016 Oct; 48(5):846-849. PubMed ID: 27752168 [TBL] [Abstract][Full Text] [Related]
28. Skeletal muscle sonography: a correlative study of echogenicity and morphology. Reimers K; Reimers CD; Wagner S; Paetzke I; Pongratz DE J Ultrasound Med; 1993 Feb; 12(2):73-7. PubMed ID: 8468739 [TBL] [Abstract][Full Text] [Related]
29. Segmentation of anterior cruciate ligament in knee MR images using graph cuts with patient-specific shape constraints and label refinement. Lee H; Hong H; Kim J Comput Biol Med; 2014 Dec; 55():1-10. PubMed ID: 25305694 [TBL] [Abstract][Full Text] [Related]
30. The relation between local and distal muscle fat infiltration in chronic whiplash using magnetic resonance imaging. Karlsson A; Peolsson A; Elliott J; Romu T; Ljunggren H; Borga M; Dahlqvist Leinhard O PLoS One; 2019; 14(12):e0226037. PubMed ID: 31805136 [TBL] [Abstract][Full Text] [Related]
31. Automatic quantification of epicardial fat volume on non-enhanced cardiac CT scans using a multi-atlas segmentation approach. Shahzad R; Bos D; Metz C; Rossi A; Kirisli H; van der Lugt A; Klein S; Witteman J; de Feyter P; Niessen W; van Vliet L; van Walsum T Med Phys; 2013 Sep; 40(9):091910. PubMed ID: 24007161 [TBL] [Abstract][Full Text] [Related]
32. Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification. Shahid ML; Chitiboi T; Ivanovska T; Molchanov V; Völzke H; Linsen L BMC Med Imaging; 2017 Feb; 17(1):15. PubMed ID: 28196476 [TBL] [Abstract][Full Text] [Related]
33. Generating color-coded anatomic muscle maps for correlation of quantitative magnetic resonance imaging analysis with clinical examination in neuromuscular disorders. Javan R; Horvath JJ; Case LE; Austin S; Corderi J; Dubrovsky A; Kishnani PS; Bashir MR Muscle Nerve; 2013 Aug; 48(2):293-5. PubMed ID: 23801454 [TBL] [Abstract][Full Text] [Related]
34. Deep learning for automatic segmentation of thigh and leg muscles. Agosti A; Shaqiri E; Paoletti M; Solazzo F; Bergsland N; Colelli G; Savini G; Muzic SI; Santini F; Deligianni X; Diamanti L; Monforte M; Tasca G; Ricci E; Bastianello S; Pichiecchio A MAGMA; 2022 Jun; 35(3):467-483. PubMed ID: 34665370 [TBL] [Abstract][Full Text] [Related]
35. Manually defining regions of interest when quantifying paravertebral muscles fatty infiltration from axial magnetic resonance imaging: a proposed method for the lumbar spine with anatomical cross-reference. Crawford RJ; Cornwall J; Abbott R; Elliott JM BMC Musculoskelet Disord; 2017 Jan; 18(1):25. PubMed ID: 28103921 [TBL] [Abstract][Full Text] [Related]
37. Evaluation of an automated thresholding algorithm for the quantification of paraspinal muscle composition from MRI images. Fortin M; Omidyeganeh M; Battié MC; Ahmad O; Rivaz H Biomed Eng Online; 2017 May; 16(1):61. PubMed ID: 28532491 [TBL] [Abstract][Full Text] [Related]
38. An Investigation of Fat Infiltration of the Multifidus Muscle in Patients With Severe Neck Symptoms Associated With Chronic Whiplash-Associated Disorder. Karlsson A; Leinhard OD; Åslund U; West J; Romu T; Smedby Ö; Zsigmond P; Peolsson A J Orthop Sports Phys Ther; 2016 Oct; 46(10):886-893. PubMed ID: 27590177 [TBL] [Abstract][Full Text] [Related]
39. Automatic quantification of muscle volumes in magnetic resonance imaging scans of the lower extremities. Brunner G; Nambi V; Yang E; Kumar A; Virani SS; Kougias P; Shah D; Lumsden A; Ballantyne CM; Morrisett JD Magn Reson Imaging; 2011 Oct; 29(8):1065-75. PubMed ID: 21855242 [TBL] [Abstract][Full Text] [Related]