生物成像和整个生物体发育生理学的未来
Oliver Tills1, Ziad Ibbini1, John I Spicer1
1Ecophysiology and Development Research Group, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, UK.
概括
先进的计算机视觉和深度学习可以克服图像分析中的瓶,使各种物种的发育生理学的综合现象学成为可能. 这项技术有望彻底改变我们对生物生长和功能的理解.
科学领域:
- 发展生物学 发展生物学
- 计算生物学 计算生物学
- 生理学 生理学 生理学
背景情况:
- 奥米克技术已经迅速发展,但整个生物体的现象学,特别是发育生理学,落后了.
- 目前的研究在图像分析方面面临瓶,这阻碍了用于研究生物发育的成像.
研究的目的:
- 评估成像对于测量整个生物体发育生理学的重要性.
- 探索先进图像分析的潜力,包括深度学习和计算机视觉,以推进现象学.
- 识别应用计算机视觉到发育生理学的挑战和机会.
主要方法:
- 关于计算机视觉和深度学习在发育生物学中的应用的审查和评论.
- 图像采集和图像分析在生物研究中的瓶的分析.
- 探索计算机视觉模型跨物种,生命阶段和种类的可转移性.
主要成果:
- 复杂的图像分析,特别是深度学习和计算机视觉,为推进对发展中动物的理解提供了重大潜力.
- 计算机视觉工具可以跨物种和实验环境转移,与许多特定物种的方法不同.
- 在将计算机视觉应用到发育生理学中的六个关键挑战和机会被确定.
结论:
- 计算机视觉正在从手动图像分析替代到探索复杂发育生物学的强大方法.
- 可转移的计算机视觉模型可以推动对发育生理学的现象学的采用.
- 这种方法使得在广泛的发育生物体中能够量化整合生理学.
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