Ruffini Nicolas, Altahini Saleh, Weißbach Stephan, Weber Nico, Milkovits Jonas, Wierczeiko Anna, Backhaus Hendrik, Stroh Albrecht
Institute for Human Genetics, University Medical Center, Johannes Gutenberg University, 55131 Mainz, Germany.
Leibniz Institute for Resilience Research, Leibniz Association, 55122 Mainz, Germany.
Bioinformatics. 2024 Mar 29;40(4). doi: 10.1093/bioinformatics/btae177.
Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio. This has led to a reliance on manual segmentation techniques. However, manual methods, while customized to specific laboratory protocols, can introduce variability due to individual differences in interpretation, potentially affecting dataset consistency across studies. In response to this challenge, we present ViNe-Seg: a deep-learning-based semi-automatic segmentation tool that offers (i) detection of visible neurons, irrespective of their activity status; (ii) the ability to perform segmentation during an ongoing experiment; (iii) a user-friendly graphical interface that facilitates expert supervision, ensuring precise identification of Regions of Interest; (iv) an array of segmentation models with the option of training custom models and sharing them with the community; and (v) seamless integration of subsequent analysis steps.
ViNe-Seg code and documentation are publicly available at https://github.com/NiRuff/ViNe-Seg and can be installed from https://pypi.org/project/ViNeSeg/.
在神经元微电路光学功能成像分析中，神经细胞体的分割是关键步骤，通常也是最耗时的步骤。近年来，已经开发了多种自动分割工具来提高分割过程的速度和一致性，主要是使用深度学习方法。当前的分割工具虽然先进，但在产生准确的分割结果方面仍面临挑战，尤其是在信噪比较低的数据集中。这导致了对手动分割技术的依赖。然而，手动方法虽然是根据特定实验室协议定制的，但由于个体解释差异可能会引入变异性，从而可能影响不同研究数据集的一致性。为应对这一挑战，我们提出了ViNe-Seg：一种基于深度学习的半自动分割工具，它具有以下特点：（i）能够检测可见神经元，无论其活动状态如何；（ii）能够在正在进行的实验中进行分割；（iii）用户友好的图形界面，便于专家监督，确保精确识别感兴趣区域；（iv）一系列分割模型，可选择训练自定义模型并与社区共享；以及（v）无缝集成后续分析步骤。
ViNe-Seg代码和文档可在https://github.com/NiRuff/ViNe-Seg上公开获取，并可从https://pypi.org/project/ViNeSeg/进行安装。
