AUTOMATED SEGMENTATION OF FLUORESCENCE MICROSCOPY DATA SETS

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DESCRIPTION (provided by applicant): In recent years, the focus in biological science has shifted to understanding complex systems at the cellular and molecular levels, a task greatly facilitated by fluorescence microscopy. Its success is due in part to the advent of a range of new fluorescent probes used to tag proteins or molecules of interest, including the nontoxic, green fluorescent protein (GFP). While fluorescence microscopes permit the collection of large, high-dimensional data sets, their manual processing is inefficient, not reproducible, time-consuming and error-prone, prompting the movement towards automated, efficient and robust processing for high-throughput applications. Segmentation, a fundamental, yet very difficult problem in image processing, is often the first processing step following acquisition. While it is always desirable for imaging tasks in biology to be as automated as possible, this is especially critical for segmentation, as it takes human experts anywhere from hours to days to segment by hand. The current segmentation algorithm used in fluorescence microscopy - the watershed algorithm - is not well-suited to this problem. Meanwhile, state-of-the-art segmentation algorithms have only recently begun to be applied to this problem. We will work both on a specific biological problem of Golgi study, as well as other fluorescence microscope data sets provided by our collaborators. Thus: We propose to develop a flexible framework, a family of algorithms and a software toolbox for the automated segmentation of fluorescence microscope images based on multiscale transformations and active contour methods. We plan on pursuing this goal through the following three specific aims: 7 Specific Aim M: Develop a class of multiscale active contour transformations to efficiently extract those features of the fluorescence microscope data needed for segmentation and develop a class of energy functionals and a corresponding family of segmentation algorithms that is flexible, modular and has an efficient implementation. 7 Specific Aim D: Develop different algorithmic modules to cater to data-specific issues pertaining to initialization, computation of the forces, topology preservation and multiresolution transformation, and nature of the data such as multidimensionality/tissue images, as well as auxiliary modules specific to the application. 7 Specific Aim S: Develop a flexible software platform and a user-friendly GUI to facilitate use by biologists as well as interaction between biologists and algorithm developers. The motivation is for this family of algorithms to be used for segmentation of fluorescence microscope data sets, as these are widely used to study processes at molecular and cellular levels. As segmentation is a typical first step in the analysis of such data sets, robust and automated segmentation algorithms are a must to enable large-scale studies of molecular and cellular processes.