Background

During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro.

New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure.

Results

In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains.

Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth.

Conclusions

NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay.

中文翻译：

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NeuriteNet：用于评估神经突生长形态参数的卷积神经网络

背景

在发育或再生过程中，神经元通过可以在培养中容易分析的机制扩展过程（即，神经突）。然而，由于体外神经突生长的复杂树枝化和异质模式，确定药物或基因操作对此类机制的影响可能具有挑战性。

新方法：NeuriteNet 是一种卷积神经网络 (CNN) 排序模型，它使用 XRAI 显着性图叠加的新适应，这是一种基于区域的归因方法。NeuriteNet 根据神经突生长模式的差异比较神经元群体，将它们分类到各自的组中，并覆盖一个显着性图，指示哪些区域区分了图像以用于分类过程。

结果

在这项研究中，我们证明了 NeuriteNet 根据已知的形态差异有效地将与解离的神经元相对应的图像分类为对照组和治疗组。此外，在将图像分类为治疗组时，显着性图叠加突出了神经元的显着特征。NeuriteNet 还确定了从对照和转基因小鼠品系培养的神经元的新形态差异。

与现有方法的比较：与其他神经突分析平台不同，NeuriteNet 不需要手动操作，例如在分析之前分割神经突，并且比经验丰富的研究人员更准确地根据神经突生长模式对神经元进行分类。

结论

NeuriteNet 可用于有效筛选异构神经元组中的形态差异，并通过显着性图叠加提供关于区分这些组的关键特征的反馈。