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DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.
Zargari, Abolfazl; Lodewijk, Gerrald A; Mashhadi, Najmeh; Cook, Nathan; Neudorf, Celine W; Araghbidikashani, Kimiasadat; Hays, Robert; Kozuki, Sayaka; Rubio, Stefany; Hrabeta-Robinson, Eva; Brooks, Angela; Hinck, Lindsay; Shariati, S Ali.
Afiliação
  • Zargari A; Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Lodewijk GA; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Mashhadi N; Department of Computer Science and Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Cook N; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Neudorf CW; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Araghbidikashani K; Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Hays R; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Kozuki S; Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Rubio S; Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Hrabeta-Robinson E; Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Brooks A; Institute for the Biology of Stem Cells, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Hinck L; Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, USA.
  • Shariati SA; Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Cell Rep Methods ; 3(6): 100500, 2023 06 26.
Article em En | MEDLINE | ID: mdl-37426758
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Microscopia Idioma: En Revista: Cell Rep Methods Ano de publicação: 2023 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / Microscopia Idioma: En Revista: Cell Rep Methods Ano de publicação: 2023 Tipo de documento: Article