Automated mesenchymal stem cell segmentation and machine learning-based phenotype classification using morphometric and textural analysis.

Mota, Sakina M; Rogers, Robert E; Haskell, Andrew W; McNeill, Eoin P; Kaunas, Roland; Gregory, Carl A; Giger, Maryellen L; Maitland, Kristen C

Mota, Sakina M; Rogers, Robert E; Haskell, Andrew W; McNeill, Eoin P; Kaunas, Roland; Gregory, Carl A; Giger, Maryellen L; Maitland, Kristen C.

Afiliação

Mota SM; Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.
Rogers RE; Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States.
Haskell AW; Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States.
McNeill EP; Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States.
Kaunas R; Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States.
Gregory CA; Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States.
Giger ML; Texas A&M Health Science Center, College of Medicine, Bryan, Texas, United States.
Maitland KC; University of Chicago, Department of Radiology, Committee on Medical Physics, Chicago, Illinois, United States.

J Med Imaging (Bellingham) ; 8(1): 014503, 2021 Jan.

Article em En | MEDLINE | ID: mdl-33542945

Palavras-chave

cell phenotype classification; image segmentation; machine learning; monolayer cell culture; stem cell; viability assessment

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: J Med Imaging (Bellingham) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

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