School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK.
School of Biosciences, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK.
Development. 2023 Nov 15;150(22). doi: 10.1242/dev.202068. Epub 2023 Nov 16.
Recent work shows that the developmental potential of progenitor cells in the HH10 chick brain changes rapidly, accompanied by subtle changes in morphology. This demands increased temporal resolution for studies of the brain at this stage, necessitating precise and unbiased staging. Here, we investigated whether we could train a deep convolutional neural network to sub-stage HH10 chick brains using a small dataset of 151 expertly labelled images. By augmenting our images with biologically informed transformations and data-driven preprocessing steps, we successfully trained a classifier to sub-stage HH10 brains to 87.1% test accuracy. To determine whether our classifier could be generally applied, we re-trained it using images (269) of randomised control and experimental chick wings, and obtained similarly high test accuracy (86.1%). Saliency analyses revealed that biologically relevant features are used for classification. Our strategy enables training of image classifiers for various applications in developmental biology with limited microscopy data.
最近的研究表明，HH10 鸡胚大脑中的祖细胞的发育潜力迅速变化，同时形态也发生微妙变化。这就要求在这个阶段对大脑进行更高时间分辨率的研究，因此需要精确和无偏的分期。在这里，我们研究了是否可以使用一个由 151 张经过专家标记的图像组成的小数据集，训练一个深度卷积神经网络来对 HH10 鸡胚大脑进行亚分期。通过用具有生物学意义的变换和数据驱动的预处理步骤来扩充我们的图像，我们成功地训练了一个分类器，将 HH10 大脑的亚分期达到 87.1%的测试准确性。为了确定我们的分类器是否可以通用，我们使用随机控制和实验鸡翅膀的图像（269 张）重新训练了它，并获得了类似的高测试准确性（86.1%）。显著度分析表明，生物相关的特征被用于分类。我们的策略使具有有限显微镜数据的发育生物学的各种应用的图像分类器的训练成为可能。
