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ilastik: interactive machine learning for (bio)image analysis.
Berg, Stuart; Kutra, Dominik; Kroeger, Thorben; Straehle, Christoph N; Kausler, Bernhard X; Haubold, Carsten; Schiegg, Martin; Ales, Janez; Beier, Thorsten; Rudy, Markus; Eren, Kemal; Cervantes, Jaime I; Xu, Buote; Beuttenmueller, Fynn; Wolny, Adrian; Zhang, Chong; Koethe, Ullrich; Hamprecht, Fred A; Kreshuk, Anna.
Afiliação
  • Berg S; HHMI Janelia Research Campus, Ashburn, Virginia, USA.
  • Kutra D; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Kroeger T; European Molecular Biology Laboratory, Heidelberg, Germany.
  • Straehle CN; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Kausler BX; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Haubold C; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Schiegg M; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Ales J; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Beier T; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Rudy M; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Eren K; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Cervantes JI; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Xu B; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Beuttenmueller F; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Wolny A; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Zhang C; European Molecular Biology Laboratory, Heidelberg, Germany.
  • Koethe U; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Hamprecht FA; HCI/IWR, Heidelberg University, Heidelberg, Germany.
  • Kreshuk A; HCI/IWR, Heidelberg University, Heidelberg, Germany.
Nat Methods ; 16(12): 1226-1232, 2019 12.
Article em En | MEDLINE | ID: mdl-31570887
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a nonlinear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Nat Methods Assunto da revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Aprendizado de Máquina Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Nat Methods Assunto da revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Estados Unidos