23 March 2016 Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification
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Abstract
Our group is developing a method to examine biological specimens in cellular detail using synchrotron microCT. The method can acquire 3D images of tissue at micrometer-scale resolutions, allowing for individual cell types to be visualized in the context of the entire specimen. For model organism research, this tool will enable the rapid characterization of tissue architecture and cellular morphology from every organ system. This characterization is critical for proposed and ongoing “phenome” projects that aim to phenotype whole-organism mutants and diseased tissues from different organisms including humans. With the envisioned collection of hundreds to thousands of images for a phenome project, it is important to develop quantitative image analysis tools for the automated scoring of organism phenotypes across organ systems. Here we present a first step towards that goal, demonstrating the use of support vector machines (SVM) in detecting retinal cell nuclei in 3D images of wild-type zebrafish. In addition, we apply the SVM classifier on a mutant zebrafish to examine whether SVMs can be used to capture phenotypic differences in these images. The longterm goal of this work is to allow cellular and tissue morphology to be characterized quantitatively for many organ systems, at the level of the whole-organism.
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Yifu Ding, Thomas Tavolara, Keith Cheng, "Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97911A (23 March 2016); doi: 10.1117/12.2216940; https://doi.org/10.1117/12.2216940
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