Bass Cher, Helkkula Pyry, De Paola Vincenzo, Clopath Claudia, Bharath Anil Anthony
Centre for Neurotechnology, South Kensington Campus, Imperial College London, London, United Kingdom.
Department of Bioengineering, South Kensington Campus, Imperial College London, London, United Kingdom.
PLoS One. 2017 Sep 5;12(9):e0183309. doi: 10.1371/journal.pone.0183309. eCollection 2017.
Studies of structural plasticity in the brain often require the detection and analysis of axonal synapses (boutons). To date, bouton detection has been largely manual or semi-automated, relying on a step that traces the axons before detection the boutons. If tracing the axon fails, the accuracy of bouton detection is compromised. In this paper, we propose a new algorithm that does not require tracing the axon to detect axonal boutons in 3D two-photon images taken from the mouse cortex. To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. We found that our method achieved a Recall of 95%, Precision of 76%, and F1 score of 84% within a new dataset that we make available for accessing bouton detection. On average, Recall and F1 score were significantly better than the current state-of-the-art method, while Precision was not significantly different. In conclusion, in this article we demonstrate that our approach, which is independent of axon tracing, can detect boutons to a high level of accuracy, and improves on the detection performance of existing approaches. The data and code (with an easy to use GUI) used in this article are available from open source repositories.
对大脑结构可塑性的研究通常需要检测和分析轴突突触（终扣）。迄今为止，终扣检测大多是手动或半自动的，依赖于在检测终扣之前追踪轴突的步骤。如果轴突追踪失败，终扣检测的准确性就会受到影响。在本文中，我们提出了一种新算法，该算法在从小鼠皮层获取的三维双光子图像中检测轴突终扣时无需追踪轴突。为了找到最适合此任务的技术，我们比较了几种用于兴趣点检测和特征描述符生成的著名算法。所提出的最终算法有以下主要步骤：（1）基于高斯拉普拉斯（LoG）的特征增强模块，以突出终扣的外观；（2）加速鲁棒特征（SURF）兴趣点检测器，以找到特征提取的候选位置；（3）非极大值抑制，以消除在同一局部区域被多次检测到的候选对象；（4）基于Gabor滤波器生成特征描述符；（5）支持向量机（SVM）分类器，使用来自标记数据的特征进行训练，并用于区分终扣候选对象和非终扣候选对象。我们发现，在我们提供的用于访问终扣检测的新数据集中，我们的方法实现了95%的召回率、76%的精确率和84%的F1分数。平均而言，召回率和F1分数显著优于当前的最先进方法，而精确率没有显著差异。总之，在本文中我们证明，我们的方法独立于轴突追踪，可以高精度地检测终扣，并提高了现有方法的检测性能。本文中使用的数据和代码（带有易于使用的图形用户界面）可从开源存储库中获取。
