Background

Analyzing images to accurately estimate the number of different cell types in the brain using automatic methods is a major objective in neuroscience. The automatic and selective detection and segmentation of neurons would be an important step in neuroanatomical studies.

New method

We present a method to improve the 3D reconstruction of neuronal nuclei that allows their segmentation, excluding the nuclei of non-neuronal cell types.

Results

We have tested the algorithm on stacks of images from rat neocortex, in a complex scenario (large stacks of images, uneven staining, and three different channels to visualize different cellular markers). It was able to provide a good identification ratio of neuronal nuclei and a 3D segmentation.

Comparison with Existing Methods: Many automatic tools are in fact currently available, but different methods yield different cell count estimations, even in the same brain regions, due to differences in the labeling and imaging techniques, as well as in the algorithms used to detect cells. Moreover, some of the available automated software methods have provided estimations of cell numbers that have been reported to be inaccurate or inconsistent after evaluation by neuroanatomists.

Conclusions

It is critical to have a tool for automatic segmentation that allows discrimination between neurons, glial cells and perivascular cells. It would greatly speed up a task that is currently performed manually and would allow the cell counting to be systematic, avoiding human bias. Furthermore, the resulting 3D reconstructions of different cell types can be used to generate models of the spatial distribution of cells.

中文翻译：

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使用多通道信息进行神经元核的 3D 分割和细胞类型识别

背景

使用自动方法分析图像以准确估计大脑中不同细胞类型的数量是神经科学的一个主要目标。神经元的自动和选择性检测和分割将是神经解剖学研究的重要一步。

新方法

我们提出了一种改进神经元细胞核 3D 重建的方法，该方法允许它们的分割，不包括非神经元细胞类型的细胞核。

结果

我们已经在复杂的场景（大量图像、不均匀染色和三个不同的通道来可视化不同的细胞标记）中测试了来自大鼠新皮层的成堆图像的算法。它能够提供良好的神经元核识别率和 3D 分割。

与现有方法的比较：实际上目前有许多自动工具可用，但由于标记和成像技术以及用于检测细胞的算法不同，不同的方法会产生不同的细胞计数估计，即使在相同的大脑区域也是如此. 此外，一些可用的自动化软件方法提供了细胞数量的估计，据报道，在神经解剖学家评估后，这些估计不准确或不一致。

结论

拥有一种允许区分神经元、神经胶质细胞和血管周围细胞的自动分割工具至关重要。它将大大加快目前手动执行的任务，并使细胞计数系统化，避免人为偏见。此外，由此产生的不同细胞类型的 3D 重建可用于生成细胞空间分布的模型。