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Status : Active
Field of Research : Image Processing
Scheme : ARC Future Fellowships
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  • Researchers (18)
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  • Active Funded Activity

    ARC Future Fellowships - Grant ID: FT190100516

    Funder
    Australian Research Council
    Funding Amount
    $746,380.00
    Summary
    Regulation of 3D Cell Migration by Microtubule-Dependent Processes. The overarching aim of this research is to elucidate the molecular mechanisms that cells use to move in 3D environments: a basic biological function essential to development and homeostasis. During these processes, cells interact with their surroundings where they translate biophysical forces into biochemical signals to adapt their shape to move. This requires distinct signalling, controlled in space and time, to regulate the cr .... Regulation of 3D Cell Migration by Microtubule-Dependent Processes. The overarching aim of this research is to elucidate the molecular mechanisms that cells use to move in 3D environments: a basic biological function essential to development and homeostasis. During these processes, cells interact with their surroundings where they translate biophysical forces into biochemical signals to adapt their shape to move. This requires distinct signalling, controlled in space and time, to regulate the crosstalk between organelles and the cytoskeleton. To date, the role of microtubules remains elusive. Using interdisciplinary approaches combining advanced imaging technology with novel cell biology methods, the project aims to uncover fundamental knowledge about how cells interact with their environment.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT200100899

    Funder
    Australian Research Council
    Funding Amount
    $811,452.00
    Summary
    Imaging the foundation of the nervous system. This Project aims to understand the formation of the neural tube; a fundamental tissue structure that generates the brain and the spinal cord. Using interdisciplinary approaches and exploiting recent advances in transgenic and imaging technologies, the Project expects to reveal the complex interplay of molecular, cellular and mechanical processes that direct neural tissue formation and cell fate specification. Outcomes from the Project include knowle .... Imaging the foundation of the nervous system. This Project aims to understand the formation of the neural tube; a fundamental tissue structure that generates the brain and the spinal cord. Using interdisciplinary approaches and exploiting recent advances in transgenic and imaging technologies, the Project expects to reveal the complex interplay of molecular, cellular and mechanical processes that direct neural tissue formation and cell fate specification. Outcomes from the Project include knowledge of previously intractable developmental processes, training of future scientists and development of international collaborations. This should provide enhanced imaging capacity, a higher quality scientific workforce and position Australia at the forefront of developmental biology.
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    Active Funded Activity

    ARC Future Fellowships - Grant ID: FT190100197

    Funder
    Australian Research Council
    Funding Amount
    $735,000.00
    Summary
    Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin .... Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.
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