Background and objective

Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application of this technique to AD mouse brain enables us to investigate neuron changes during the progression of AD pathology. However, how to deal with the huge amount of data becomes the bottleneck.

Methods

Using MOST technology, we acquired 3D whole-brain images of six AD mice, and sampled the imaging data of four regions in each mouse brain for AD progression analysis. To count the number of neurons, we proposed a deep learning based method by detecting neuronal soma in the neuronal images. In our method, the neuronal images were first cut into small cubes, then a Convolutional Neural Network (CNN) classifier was designed to detect the neuronal soma by classifying the cubes into three categories, “soma”, “fiber”, and “background”.

Results

Compared with the manual method and currently available NeuroGPS software, our method demonstrates faster speed and higher accuracy in identifying neurons from the MOST images. By applying our method to various brain regions of 6-month-old and 12-month-old AD mice, we found that the amount of neurons in three brain regions (lateral entorhinal cortex, medial entorhinal cortex, and presubiculum) decreased slightly with the increase of age, which is consistent with the experimental results previously reported.

Conclusion

This paper provides a new method to automatically handle the huge amounts of data and accurately identify neuronal soma from the MOST images. It also provides the potential possibility to construct a whole-brain neuron projection to reveal the impact of AD pathology on mouse brain.

中文翻译：

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基于深度学习的神经元体细胞检测和计数，用于阿尔茨海默氏病分析

背景和目标

阿尔茨海默氏病（AD）与神经元损害和减少有关。微光学断层扫描（MOST）提供了一种获取高分辨率图像以进行全脑神经元分析的方法。该技术在AD小鼠大脑中的应用使我们能够研究AD病理过程中神经元的变化。但是，如何处理海量数据成为瓶颈。

方法

使用MOST技术，我们获得了六只AD小鼠的3D全脑图像，并对每个小鼠大脑四个区域的成像数据进行了采样，以进行AD进展分析。为了计算神经元的数量，我们提出了一种基于深度学习的方法，即通过检测神经元图像中的神经元体。在我们的方法中，首先将神经元图像切成小方块，然后设计卷积神经网络（CNN）分类器，通过将立方体分为“ soma”，“纤维”和“背景”三类来检测神经元体细胞。 。

结果

与手动方法和当前可用的NeuroGPS软件相比，我们的方法在从MOST图像中识别神经元方面展示了更快的速度和更高的准确性。通过将我们的方法应用于6个月大和12个月大的AD小鼠的各个大脑区域，我们发现三个大脑区域（外侧内嗅皮层，内侧内嗅皮层和前屈皮层）中的神经元数量均随着年龄的增加，这与先前报道的实验结果一致。

结论

本文提供了一种自动处理大量数据并从MOST图像中准确识别神经元体的新方法。它还提供了构建全脑神经元投影以揭示AD病理学对小鼠大脑的影响的潜在可能性。