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BrainLine: An Open Pipeline for Connectivity Analysis of Heterogeneous Whole-Brain Fluorescence Volumes.
Athey, Thomas L; Wright, Matthew A; Pavlovic, Marija; Chandrashekhar, Vikram; Deisseroth, Karl; Miller, Michael I; Vogelstein, Joshua T.
Afiliação
  • Athey TL; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Wright MA; Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, USA.
  • Pavlovic M; Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA.
  • Chandrashekhar V; Department of Bioengineering, Stanford University, Stanford, CA, USA.
  • Deisseroth K; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Miller MI; Department of Bioengineering, Stanford University, Stanford, CA, USA.
  • Vogelstein JT; CNC Program, Stanford University, Stanford, CA, USA.
bioRxiv ; 2023 Mar 01.
Article em En | MEDLINE | ID: mdl-36909631
ABSTRACT
Whole-brain fluorescence images require several stages of computational processing to fully reveal the neuron morphology and connectivity information they contain. However, these computational tools are rarely part of an integrated pipeline. Here we present BrainLine, an open-source pipeline that interfaces with existing software to provide registration, axon segmentation, soma detection, visualization and analysis of results. By implementing a feedback based training paradigm with BrainLine, we were able to use a single learning algorithm to accurately process a diverse set of whole-brain images generated by light-sheet microscopy. BrainLine is available as part of our Python package brainlit http//brainlit.neurodata.io/ .

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article