New Image Analysis Model Could Advance Research in Obesity, Related Health Issues

Thu, 05/09/2013 - 23:00 | Atlanta, GA

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Joshua Preston

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Researchers in the H. Milton Stewart School of Industrial & Systems Engineering (ISyE) and School of Chemical and Biomolecular Engineering (ChBE) are developing an image processing system that can automate the identification of a species of worm, Caenorhabditis elegans, and that presents a good model for studying the genetic components of health issues, such as obesity, according to the researchers.

The project is jointly supervised by Xiaoming Huo from ISyE and Hang Lu from ChBE, and the Institute for Data and High Performance Computing is providing seed funding for a graduate research assistant in ISyE to help advance the research. The model is being developed as a new platform that will allow for accurate phenotyping or classification of characteristics in the worms using high-throughput computing to determine the genes and pathways as well as compositions in food intake that contribute to fat accumulation.

The main objective is to develop the image processing system with pattern recognition to automatically distinguish the distinct types of lipid droplets, which are composed of fatty acid compounds, in the worms. The image analysis and classification system will systematically extract image features, efficiently learn models, and reliably predict phenotypes, or characteristics, from the images that are developed by studying the lipid droplets.

Current limitations in imaging and analysis of the lipid droplets in the worms have stunted the potential for growth, exploration, and attainable knowledge in the lipid droplet realm of research, says co-principal investigator Xiaoming Huo in ISyE.

Current methods used by the team enable them to obtain only one set of 3D images every ten seconds. A comprehensive study on the relationship between food composition and the resulting lipid analysis requires the ability to identify and classify the characteristics of hundreds of thousands of images. Researchers say that such high throughput is only manageable if the image processing and consequent prediction is automated.

The proposed research has direct applications in other problems in biology, such as neural development, stem cells, cancer diagnosis, and drug discovery. It is also potentially applicable in areas such as contemporary manufacturing of advanced nanomaterial, where a core problem is predicting the properties of produced nanomaterial.

“The research is potentially transformative because the proposed approach will develop a new technique for quantitative imaging, high-throughput experimentation, and analysis of lipid distribution and protein function in C. elegans, in pursuit of determining the unknown genetic contribution to fat storage and distribution,” says co-principal investigator Hang Lu in ChBE.

Part of the process involves microfluidics, sometimes called “Lab-on-a-Chip,” and used in the project for imaging, manipulating and sorting the animals. Combined with the statistical image analysis methods funded through the IDH seed grant, the researchers aspire to move the frontier of genetic research to the next level.