Fudenberg Geoffrey, Paninski Liam
Department of Statistics and Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA.
IEEE Trans Image Process. 2009 Mar;18(3):471-82. doi: 10.1109/TIP.2008.2010212.
Experimental research seeking to quantify neuronal structure constantly contends with restrictions on image resolution and variability. In particular, experimentalists often need to analyze images with very low signal-to-noise ratio (SNR). In many experiments, dye toxicity scales with the light intensity; this leads experimentalists to reduce image SNR in order to preserve the viability of the specimen. In this paper, we present a Bayesian approach for estimating the neuronal shape given low-SNR observations. This Bayesian framework has two major advantages. First, the method effectively incorporates known facts about 1) the image formation process, including blur and the Poisson nature of image noise at low intensities, and 2) dendritic shape, including the fact that dendrites are simply-connected geometric structures with smooth boundaries. Second, we may employ standard Markov chain Monte Carlo techniques for quantifying the posterior uncertainty in our estimate of the dendritic shape. We describe an efficient computational implementation of these methods and demonstrate the algorithm's performance on simulated noisy two-photon laser-scanning microscopy images.
旨在量化神经元结构的实验研究一直面临着图像分辨率和变异性方面的限制。特别是，实验人员常常需要分析信噪比（SNR）极低的图像。在许多实验中，染料毒性随光强度而变化；这使得实验人员为了保持样本的活力而降低图像的信噪比。在本文中，我们提出了一种贝叶斯方法，用于在低信噪比观测条件下估计神经元形状。这个贝叶斯框架有两个主要优点。首先，该方法有效地纳入了关于以下两方面的已知事实：1）图像形成过程，包括模糊以及低强度下图像噪声的泊松性质；2）树突形状，包括树突是具有光滑边界的简单连通几何结构这一事实。其次，我们可以采用标准的马尔可夫链蒙特卡罗技术来量化我们对树突形状估计中的后验不确定性。我们描述了这些方法的一种高效计算实现方式，并在模拟的有噪声双光子激光扫描显微镜图像上展示了该算法的性能。
