Digital image analysis is a necessary component of modern microscopy, capable of turning microscopy into a truly quantitative tool, and facilitating the evaluation and quantitative analysis of image data too large and complex to be analyzed by any other means.
Over the past 15 years, the O’Brien Center has pioneered intravital and 3-dimensional imaging of renal tissues, with the Digital Image Analysis Core providing consultation, training and software development to facilitate the exploration and quantitative analysis of microscopy data. Over that time, members of the Digital Image Analysis core have become familiar with the unique characteristics of the image data collected in these kinds of studies and the challenges that these data pose to traditional methods of image analysis. In particular, microscopy data are generally incompatible with standard methods of edge detection and segmentation, the necessary steps preliminary to quantification. In the absence of effective methods of automated segmentation, researchers are left with the onerous task of manual image segmentation, a process so time-consuming that it discourages investigators from conducting quantitative microscopy.
The primary goal of the core is to develop and implement methods of image segmentation designed to address the challenges of fluorescence microscopy data, providing investigators with the tools necessary to conduct quantitative analyses of fluorescence image data.