Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China.
School of Future Technology, University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China.
BMC Bioinformatics. 2018 Jul 13;19(1):263. doi: 10.1186/s12859-018-2232-0.
The locations and shapes of synapses are important in reconstructing connectomes and analyzing synaptic plasticity. However, current synapse detection and segmentation methods are still not adequate for accurately acquiring the synaptic connectivity, and they cannot effectively alleviate the burden of synapse validation.
We propose a fully automated method that relies on deep learning to realize the 3D reconstruction of synapses in electron microscopy (EM) images. The proposed method consists of three main parts: (1) training and employing the faster region convolutional neural networks (R-CNN) algorithm to detect synapses, (2) using the z-continuity of synapses to reduce false positives, and (3) combining the Dijkstra algorithm with the GrabCut algorithm to obtain the segmentation of synaptic clefts. Experimental results were validated by manual tracking, and the effectiveness of our proposed method was demonstrated. The experimental results in anisotropic and isotropic EM volumes demonstrate the effectiveness of our algorithm, and the average precision of our detection (92.8% in anisotropy, 93.5% in isotropy) and segmentation (88.6% in anisotropy, 93.0% in isotropy) suggests that our method achieves state-of-the-art results.
Our fully automated approach contributes to the development of neuroscience, providing neurologists with a rapid approach for obtaining rich synaptic statistics.
在重建连接组和分析突触可塑性时，突触的位置和形状很重要。然而，目前的突触检测和分割方法仍然不能准确地获取突触连接，也不能有效地减轻突触验证的负担。
我们提出了一种完全自动化的方法，该方法依赖于深度学习来实现电子显微镜（EM）图像中突触的 3D 重建。所提出的方法由三个主要部分组成：（1）训练和使用更快的区域卷积神经网络（R-CNN）算法来检测突触，（2）利用突触的 z 连续性来减少假阳性，（3）结合 Dijkstra 算法和 GrabCut 算法来获得突触间隙的分割。通过手动跟踪验证了实验结果，证明了我们提出的方法的有效性。各向异性和各向同性 EM 体的实验结果证明了我们算法的有效性，我们的检测（各向异性为 92.8%，各向同性为 93.5%）和分割（各向异性为 88.6%，各向同性为 93.0%）的平均精度表明，我们的方法达到了最先进的水平。
我们的全自动方法有助于神经科学的发展，为神经学家提供了一种快速获取丰富的突触统计信息的方法。
