III: Small: An Automatic Framework for Processing Drosophila Embryonic Images

高分辨率胚胎图像，例如，BDGP（BerkleyDrosophilaGenomeProject）数据集是发现基因间相互作用的重要工具。这些图像不仅包含基因的时间信息，而且还包含基因表达区域的精确空间信息。因此，发现基因-基因相互作用的生物学问题可以被描述为在同一发育阶段匹配胚胎表达模式的计算问题。然而，由于胚胎图像的严重成像和人为变化，设计全自动计算系统是非常具有挑战性的。目前胚胎图像处理的研究涉及大量的人工操作或仅涉及一小部分变化。在这个建议中，我提出了一个全面的自动化框架来实现三个基本任务：图像标准化，阶段确定和表达模式建模。拟议的项目将从根本上推进生物学、图像处理和模式识别以及机器学习的整合。PI将开发一系列分析模块，以标准化图像之间的差异，提供修复，并提供胚胎边界的估计。 表情模式建模将开发区分特征，以解决在不同折射情况下区分特定像素的问题。 一个关键的概念是开发一种不平衡点检测方案，该方案将最小化边缘点的出现并提供不平衡程度的度量。这些方法应该适用于分析来自其他模型物种的图像，例如，老鼠.所提出的工作将直接促进对生物图像分析至关重要的图像处理的基础和应用研究。与自然图像相比，分析发育生物图像的挑战已经对开发新的图像处理技术产生了越来越多的需求和机会。在这个项目中开发的算法和工具将提供给社区。该项目还将促进开发新课程和实验室基础设施，以便从生物数据中发现知识。

High resolution embryonic images, e.g., the data set BDGP (Berkley Drosophila Genome Project), have been introduced as an important tool for the discovery of gene-gene interaction. These images contain not only temporal information of a gene but also precise spatial information of expression regions of genes. So the biologic problem of the discovery of gene-gene interaction can be characterized as a computational problem of matching expression patterns of embryos at the same developmental stage. It is, however, very challenging to design a fully automatic computational system due to severe imaging and artificial variations in embryonic images. Current research on embryonic image processing involves significant manual manipulation or addresses only a small subset of variations. In this proposal, I propose a comprehensive automatic framework to achieve the three fundamental tasks: image standardization, stage determination, and expression pattern modeling. The proposed project will essentially advance the integration of biologic, image processing and pattern recognition, and machine learning. The PI will develop a series of analysis modules to standardize the variation across images, provide for inpainting, and provide estimates of the boundaries of embryos. The expression pattern modeling will develop discriminate features to address issues of distinguishing specific pixels in varied refraction circumstances. A key concept is to develop an imbalance point detection scheme that will minimize the occurrences of edge points and provide a measure of the imbalance degree.These methods should be adaptable for analysis of images from other model species, e.g., mouse. The proposed work will directly facilitate basic and applied research on image processing crucial to biological image analysis. The challenges in analyzing developmental biological images as compared to natural images have created increasing demands on and opportunities for developing novel image processing techniques. The algorithms and tools developed in this project will have made available to the community. This project will also facilitate the development of new courses and laboratory infrastructure for knowledge discovery from biological data.