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Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.
Hemke, Robert; Buckless, Colleen G; Tsao, Andrew; Wang, Benjamin; Torriani, Martin.
Affiliation
  • Hemke R; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6048, Boston, MA, 02114, USA.
  • Buckless CG; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Academic Medical Center, Amsterdam Movement Sciences, Amsterdam, The Netherlands.
  • Tsao A; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6048, Boston, MA, 02114, USA.
  • Wang B; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6048, Boston, MA, 02114, USA.
  • Torriani M; Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, YAW 6048, Boston, MA, 02114, USA.
Skeletal Radiol ; 49(3): 387-395, 2020 Mar.
Article in En | MEDLINE | ID: mdl-31396667
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pelvis / Body Composition / Tomography, X-Ray Computed / Neural Networks, Computer Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Middle aged Language: En Journal: Skeletal Radiol Year: 2020 Document type: Article Affiliation country: United States Country of publication: Germany
Fulltext
Add to My VHL
Print
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pelvis / Body Composition / Tomography, X-Ray Computed / Neural Networks, Computer Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Female / Humans / Male / Middle aged Language: En Journal: Skeletal Radiol Year: 2020 Document type: Article Affiliation country: United States Country of publication: Germany