Awate Suyash P, Radhakrishnan Thyagarajan
Inf Process Med Imaging. 2015;24:3-16. doi: 10.1007/978-3-319-19992-4_1.
In microscopy imaging, colocalization between two biological entities (e.g., protein-protein or protein-cell) refers to the (stochastic) dependencies between the spatial locations of the two entities in the biological specimen. Measuring colocalization between two entities relies on fluorescence imaging of the specimen using two fluorescent chemicals, each of which indicates the presence/absence of one of the entities at any pixel location. State-of-the-art methods for estimating colocalization rely on post-processing image data using an adhoc sequence of algorithms with many free parameters that are tuned visually. This leads to loss of reproducibility of the results. This paper proposes a brand-new framework for estimating the nature and strength of colocalization directly from corrupted image data by solving a single unified optimization problem that automatically deals with noise, object labeling, and parameter tuning. The proposed framework relies on probabilistic graphical image modeling and a novel inference scheme using variational Bayesian expectation maximization for estimating all model parameters, including colocalization, from data. Results on simulated and real-world data demonstrate improved performance over the state of the art.
在显微镜成像中，两个生物实体（如蛋白质-蛋白质或蛋白质-细胞）之间的共定位是指生物样本中这两个实体空间位置之间的（随机）依赖性。测量两个实体之间的共定位依赖于使用两种荧光化学物质对样本进行荧光成像，每种化学物质在任何像素位置指示其中一个实体的存在/不存在。估计共定位的现有方法依赖于使用具有许多需凭视觉调整的自由参数的临时算法序列对图像数据进行后处理。这导致结果的可重复性丧失。本文提出了一个全新的框架，通过解决一个自动处理噪声、对象标记和参数调整的单一统一优化问题，直接从损坏的图像数据中估计共定位的性质和强度。所提出的框架依赖于概率图形图像建模和一种新颖的推理方案，该方案使用变分贝叶斯期望最大化从数据中估计所有模型参数，包括共定位。在模拟数据和真实数据上的结果表明，其性能优于现有技术。
