Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.

Ahmad, Nouman; Strand, Robin; Sparresäter, Björn; Tarai, Sambit; Lundström, Elin; Bergström, Göran; Ahlström, Håkan; Kullberg, Joel

Ahmad, Nouman; Strand, Robin; Sparresäter, Björn; Tarai, Sambit; Lundström, Elin; Bergström, Göran; Ahlström, Håkan; Kullberg, Joel.

Afiliação

Ahmad N; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden. nouman.ahmad@uu.se.
Strand R; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.
Sparresäter B; Department of Information Technology, Uppsala University, Uppsala, Sweden.
Tarai S; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.
Lundström E; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.
Bergström G; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden.
Ahlström H; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Kullberg J; Department of Clinical Physiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden.

BMC Bioinformatics ; 24(1): 346, 2023 Sep 18.

Article em En | MEDLINE | ID: mdl-37723444

Assuntos

Doenças Cardiovasculares; Diabetes Mellitus Tipo 2; Humanos; Composição Corporal; Fígado; Tomografia Computadorizada por Raios X

Palavras-chave

Body composition; Computed tomography; Deep learning; Medical imaging; Segmentation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Cardiovasculares / Diabetes Mellitus Tipo 2 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Suécia

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