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Deep learning for Bioimage Analysis in Developmental Biology

Adrien Hallou, Hannah G. Yevick, Bianca Dumitrascu, Virginie Uhlmann
Development, 148(18), 2021.

Abstract

Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.

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BibTeX
@Article{publications/deep-learning-for-bioimage-analysis-in-developmental-biology, title = {Deep learning for Bioimage Analysis in Developmental Biology}, author = {Hallou, Adrien and Yevick, Hannah G. and Dumitrascu, Bianca and Uhlmann, Virginie}, journal = {Development}, year = {2021}, volume = {148}, number = {18}, doi = {10.1242/dev.199616}, url = {/publications/deep-learning-for-bioimage-analysis-in-developmental-biology.html}, abstract = {Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.} }
Endnote
%0 Journal Article %T Deep learning for Bioimage Analysis in Developmental Biology %A Adrien Hallou %A Hannah G. Yevick %A Bianca Dumitrascu %A Virginie Uhlmann %J Development %D 2021 %F publications/deep-learning-for-bioimage-analysis-in-developmental-biology %R 10.1242/dev.199616 %U /publications/deep-learning-for-bioimage-analysis-in-developmental-biology.html %V 148 %N 18 %X Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time.
RIS
TY - JOUR TI - Deep learning for Bioimage Analysis in Developmental Biology AU - Adrien Hallou AU - Hannah G. Yevick AU - Bianca Dumitrascu AU - Virginie Uhlmann DA - 2021/09/07 ID - publications/deep-learning-for-bioimage-analysis-in-developmental-biology VL - 148 IS - 18 DO - 10.1242/dev.199616 UR - /publications/deep-learning-for-bioimage-analysis-in-developmental-biology.html AB - Deep learning has transformed the way large and complex image datasets can be processed, reshaping what is possible in bioimage analysis. As the complexity and size of bioimage data continues to grow, this new analysis paradigm is becoming increasingly ubiquitous. In this Review, we begin by introducing the concepts needed for beginners to understand deep learning. We then review how deep learning has impacted bioimage analysis and explore the open-source resources available to integrate it into a research project. Finally, we discuss the future of deep learning applied to cell and developmental biology. We analyze how state-of-the-art methodologies have the potential to transform our understanding of biological systems through new image-based analysis and modelling that integrate multimodal inputs in space and time. ER -
APA
Hallou, A., Yevick, H.G., Dumitrascu, B. & Uhlmann, V.. (2021). Deep learning for Bioimage Analysis in Developmental Biology. Development 148(18) doi:10.1242/dev.199616 Available from /publications/deep-learning-for-bioimage-analysis-in-developmental-biology.html.