We propose to develop a bioinformatics toolbox to process and quantify complex muscle cell images for automated phenotype analysis. The toolbox is aimed to provide both clinical practitioners and laboratory investigators with the much-needed capability to automatically detect and identify pathological features that manifest themselves in many muscle-related diseases such as cardiomyopathy, muscular hypertrophy, amyotrophic lateral sclerosis (ALS) (often known as Lou Gehrig's disease), muscular dystrophies such as Duchenne muscular dystrophy and Becker muscular dystrophy, and inflammatory muscle damage. Many of these conditions have no effective treatment and high fatality rates. For example, ALS is progressive neurodegenerative disease that is caused by the death of motor neurons and results in increasing muscle weakness and atrophy, has a survival rate less than twenty percent over a five-year period, and the disease affects more 5,000 people in the U.S. each year. In our search for treatment for the muscle-related diseases, lack of computational method to objectively and quantitatively analyze muscle cell images has become a rate limit factor. As clinicians and researchers are increasingly looking into the cellular and molecular mechanisms of the diseases, detailed pathological analysis is necessary for people to understand the biological processes. Yet, the only available approach is manual analysis which is confined to small datasets and qualitatively interpretation of the images. Important pathological features may be missed by manual analysis or obscured due to the large variation in human observation. Also results of manual analysis are not immediately ready for data management and analysis because of the long time it takes. Hence we identified the need for a dedicated toolbox to facilitate muscle-related research, which was confirmed by our user community. Featuring novel imaging processing algorithms, the toolbox will quantitatively analyze muscle cells, integrate results from multiple channels, and export quantitative results, with little user intervention. The toolbox will advance clinical and laboratory research by providing detailed analysis of histopathological features such as the intact of cellular membrane, the location of nuclei, and geometric measurements of the cells. It will facilitate discovery by highlighting subtl yet important information in histopathology, reducing human errors, and enabling research to analyze a larger number of images than they currently are able to. The toolbox will also improve workflow in clinics and laboratories by providing users with high sensitivity, objectivity, and efficiency in interpreting muscle cell images. The quantifying capability of the toolbox will allow users to compare therapeutic treatments with a high confidence level. Overall the project will benefit the large biomedical community of treating and researching muscle-related diseases. In turn, the project will benefit the patients of muscular diseases by facilitating diagnosis of muscular disorders and discovery of new therapies.
This project will contribute to the public health by providing clinicians and researchers treating and investigating muscle-related diseases with a much-needed open-source code image analysis toolbox. The toolbox will improve the workflow of the clinicians and researchers in terms of assisting them finding important biomarkers in muscle cell images, measuring histopathological features, and reducing human errors in the process. Using innovative image processing methods to create an automated, objective, and quantitative toolbox, the project will benefit both clinical practice and laboratory investigation in a large number of biomedical fields, including cardiomyopathy, muscular hypertrophy, amyotrophic lateral sclerosis (Lou Gehrig's disease), and muscular dystrophies.
|Guo, Yanen; Xu, Xiaoyin; Wang, Yuanyuan et al. (2015) A computational approach to detect and segment cytoplasm in muscle fiber images. Microsc Res Tech 78:508-18|
|Miazaki, Mauro; Viana, Matheus P; Yang, Zhong et al. (2015) Automated high-content morphological analysis of muscle fiber histology. Comput Biol Med 63:28-35|
|Almasi, Sepideh; Xu, Xiaoyin; Ben-Zvi, Ayal et al. (2015) A novel method for identifying a graph-based representation of 3-D microvascular networks from fluorescence microscopy image stacks. Med Image Anal 20:208-23|
|Comin, Cesar Henrique; Xu, Xiaoyin; Wang, Yaming et al. (2014) An image processing approach to analyze morphological features of microscopic images of muscle fibers. Comput Med Imaging Graph 38:803-14|