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Piximi - An Images to Discovery web tool for bioimages and beyond.
Moser, Levin M; Gogoberidze, Nodar; Papaleo, Andréa; Lucas, Alice; Dao, David; Friedrich, Christoph A; Paavolainen, Lassi; Molnar, Csaba; Stirling, David R; Hung, Jane; Wang, Rex; Tromans-Coia, Callum; Li, Bin; Evans, Edward L; Eliceiri, Kevin W; Horvath, Peter; Carpenter, Anne E; Cimini, Beth A.
Afiliação
  • Moser LM; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Gogoberidze N; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Papaleo A; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Lucas A; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Dao D; ETH Zurich, Zurich, Switzerland.
  • Friedrich CA; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Paavolainen L; Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
  • Molnar C; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Stirling DR; Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC), Szeged, Hungary.
  • Hung J; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Wang R; Department of Chemical Engineering, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
  • Tromans-Coia C; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Li B; Imaging Platform, Broad Institute of MIT and Harvard, Cambridge MA, USA.
  • Evans EL; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA.
  • Eliceiri KW; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA.
  • Horvath P; Center for Quantitative Cell Imaging, University of Wisconsin-Madison, Madison, WI, USA.
  • Carpenter AE; Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC), Szeged, Hungary.
  • Cimini BA; Synthetic and Systems Biology Unit, HUN-REN Biological Research Centre (BRC), Szeged, Hungary; Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany.
bioRxiv ; 2024 Oct 10.
Article em En | MEDLINE | ID: mdl-38895349
ABSTRACT
Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article