Quantitative morphology as a marker of cellular and organismal state

最近在模式识别方面的工作表明，计算机可以赶上甚至超过人类专家的图像分类和模式分析。现代成像系统在空间和光谱分辨率以及动态范围上远远超过人眼，因此潜在地允许基于机器的图像模式分析系统超越人类执行这些任务的能力。

Recent work in pattern recognition has demonstrated that computers can equal or even surpass image classification and pattern analysis by human experts. Modern imaging systems far exceed the human eye in spatial and spectral resolution as well as dynamic range, thus potentially allowing machine-based image pattern analysis systems to surpass a human's capacity for performing these tasks. The pattern analysis system we've developed and characterized is called WND-CHARM. The approach is based on extracting over 2,000 descriptors of image content from each image using both standard feature extraction algorithms as well as those we developed ourselves. A key innovation that we introduced was to extract image content not only from the original images, but also from image transforms such as Fourier and wavelet. Each descriptor is assigned a score based on its ability to discriminate between the sets of images used in training. Thus, any image can be plotted as a point in this high-dimensional weighted feature space. The set of points representing the training images are used to model a probability density function representing the variation present in each of the training classes. The probability that a given test image belongs to each of the training classes - the marginal probabilities - can thus be determined from the image's weighted image descriptors and the probability density functions for each class. A key property of this classification mechanism is that marginal probabilities can be interpreted as image similarities, thus producing quantitative measures of similarity rather than merely qualitative classifications. A substantial effort this year was devoted to characterizing these quantitative similarity measurements. A natural test case for this is age-related morphological change. We were able to demonstrate that a morphological age calculated from images correlates well with known chronological age. The generality of our classification system has allowed us to study aging in C. elegans tissues imaged with differential interference contrast (DIC) as well as mouse tissue sections stained with hematoxylin/eosin (H+E). The ability to quantify physiological age has allowed us to characterize the aging process in much greater detail, and lead us to the discovery that age-related morphological change is not continuous, but progresses through distinct morphological states. We have continued to develop our high-density RNAi screening technology, and we have begun to characterize image data from small-scale pilot screens. The ability to quantify image similarity has allowed us to demonstrate that knock-down of genes known to have strong genetic or physical interactions leads to highly similar phenotypes. Extending this to full-genome screens will allow generating phenotypic similarity networks and characterize genes with unknown functions.