Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
Detection and classification of neurons and glial cells in the MADM mouse brain using RetinaNet.
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DOI：
10.1371/journal.pone.0257426
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发表时间：
2021
期刊：
影响因子：
3.7
通讯作者：
Greenbaum A
中科院分区：
文献类型：
--
作者：
Cai Y;Zhang X;Kovalsky SZ;Ghashghaei HT;Greenbaum A
在组织切片中自动检测和分类细胞群的能力在从发育生物学到病理学的各种应用中都是至关重要的。虽然深度学习算法被广泛应用于显微镜数据，但它们通常侧重于分割，这需要大量的训练和劳动密集型的注释。本文利用目标检测网络（神经网络）对复杂显微图像中的目标进行检测和分类，同时简化数据标注。为此，我们使用视网膜网模型对双标记镶嵌分析（Mosaic Analysis with Double Markers， MADM）小鼠大脑中的遗传标记神经元和胶质细胞进行分类。我们最初基于retinanet的模型在由MADM报告细胞表达及其表型（神经元或胶质细胞）分化的六类细胞中实现了0.90的平均精度。然而，我们发现单一的RetinaNet模型在遇到密集和饱和的胶质团时经常失败，与神经元相比，胶质团在形状和荧光团密度上表现出高度的可变性。为了克服这个问题，我们引入了第二个视网膜网络模型，专门用于检测胶质细胞簇。合并两种计算模型的预测显着提高了胶质细胞簇的自动细胞计数。提出的细胞检测工作流程将有助于细胞群体空间组织的定量分析，这不仅适用于神经科学研究的准备工作，也适用于任何含有标记细胞群体的组织准备工作。
The ability to automatically detect and classify populations of cells in tissue sections is paramount in a wide variety of applications ranging from developmental biology to pathology. Although deep learning algorithms are widely applied to microscopy data, they typically focus on segmentation which requires extensive training and labor-intensive annotation. Here, we utilized object detection networks (neural networks) to detect and classify targets in complex microscopy images, while simplifying data annotation. To this end, we used a RetinaNet model to classify genetically labeled neurons and glia in the brains of Mosaic Analysis with Double Markers (MADM) mice. Our initial RetinaNet-based model achieved an average precision of 0.90 across six classes of cells differentiated by MADM reporter expression and their phenotype (neuron or glia). However, we found that a single RetinaNet model often failed when encountering dense and saturated glial clusters, which show high variability in their shape and fluorophore densities compared to neurons. To overcome this, we introduced a second RetinaNet model dedicated to the detection of glia clusters. Merging the predictions of the two computational models significantly improved the automated cell counting of glial clusters. The proposed cell detection workflow will be instrumental in quantitative analysis of the spatial organization of cellular populations, which is applicable not only to preparations in neuroscience studies, but also to any tissue preparation containing labeled populations of cells.
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影响因子：
48
作者：
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通讯作者：
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影响因子：
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作者：
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DOI：
10.1109/tpami.2018.2858826
发表时间：
2020-02-01
影响因子：
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