Motivation To digitally reconstruct the 3D neuron morphologies has long been a major bottleneck in neuroscience. One of the obstacles to automate the procedure is the low signal-background contrast (SBC) and the large dynamic range of signal and background both within and across images. Results We developed a pipeline to enhance the neurite signal and to suppress the background, with the goal of high SBC and better within- and between-image homogeneity. The performance of the image enhancement was quantitatively verified according to the different figures of merit benchmarking the image quality. In addition, the method could improve the neuron reconstruction in approximately 1/3 of the cases, with very few cases of degrading the reconstruction. This significantly outperformed three other approaches of image enhancement. Moreover, the compression rate was increased five times by average comparing the enhanced to the raw image. All results demonstrated the potential of the proposed method in leveraging the neuroscience by providing better 3D morphological reconstruction and lower cost of data storage and transfer. Availability and implementation The study is conducted based on the Vaa3D platform and python 3.7.9. The Vaa3D platform is available on the GitHub (https://github.com/Vaa3D). The source code of the proposed image enhancement as a Vaa3D plugin, the source code to benchmark the image quality and the example image blocks are available under the repository of vaa3d_tools/hackathon/SGuo/imPreProcess. The original fMost images of mouse brains can be found at the BICCN’s Brain Image Library (BIL) (https://www.brainimagelibrary.org). Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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图像增强以利用单细胞神经元的 3D 形态重建

动机 以数字方式重建 3D 神经元形态一直是神经科学的主要瓶颈。使该过程自动化的障碍之一是低信号背景对比度 (SBC) 以及图像内和跨图像的信号和背景的大动态范围。结果我们开发了一条管道来增强神经突信号并抑制背景，目标是高 SBC 和更好的图像内和图像间均匀性。根据图像质量基准的不同品质因数，对图像增强的性能进行了定量验证。此外，该方法可以在大约 1/3 的情况下改善神经元重建，只有极少数情况下会降低重建。这明显优于其他三种图像增强方法。而且，与增强图像相比，压缩率平均增加了五倍。所有结果都证明了所提出的方法通过提供更好的 3D 形态学重建和更低的数据存储和传输成本来利用神经科学的潜力。可用性和实施​​ 该研究是基于 Vaa3D 平台和 python 3.7.9 进行的。Vaa3D 平台可在 GitHub (https://github.com/Vaa3D) 上找到。作为 Vaa3D 插件的建议图像增强的源代码、用于基准图像质量的源代码和示例图像块可在 vaa3d_tools/hackathon/SGuo/imPreProcess 的存储库下获得。小鼠大脑的原始 fMost 图像可以在 BICCN 的大脑图像库 (BIL) (https://www.brainimagelibrary.org) 中找到。