Center for Functional Connectomics, Korea Institute of Science and Technology, Seoul, Korea.
Bioinformatics. 2012 Jun 15;28(12):i25-31. doi: 10.1093/bioinformatics/bts221.
A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy.
We developed a fully automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a variational Bayesian Gaussian mixture models. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors.
The software is available on http://jinny.kist.re.kr.
一种新的技术，哺乳动物绿色荧光蛋白（GFP）在突触伙伴之间的重组（mGRASP），使得使用光学显微镜对哺乳动物的突触连接进行映射成为可能。为了描述 mGRASP 可视化的复杂神经网络中的突触的位置和分布，用高精度检测 mGRASP 荧光信号是至关重要的。
我们开发了一种用于检测 mGRASP 标记的突触小体的全自动方法。通过将每个小体建模为高斯分布，我们的方法即使在大小和形状不同的小体部分重叠的情况下也能实现准确的检测。该方法包括三个阶段：全局阈值的斑点检测；分水岭的斑点分离；和变分贝叶斯高斯混合模型的小体建模。广泛的测试表明，三阶段方法显著提高了检测精度，特别是减少了欠分割。该方法为每个检测到的斑点提供了一个拟合度评分，允许有效的错误检测。我们利用这一优势，还开发了一种高效的交互式方法来纠正错误。
该软件可在 http://jinny.kist.re.kr 上获得。
