A career development plan is proposed for Dr. David Mayerich, a computer scientist who is committed to developing an interdisciplinary career in biomedical engineering, with a focus on the collection and analysis of large-scale data sets at sub-micrometer resolution. His graduate research was in the areas of computer visualization and optical imaging, where his work lead to the development of the prototype Knife-Edge Scanning Microscope (KESM). This is the first instrument capable of imaging three-dimensional macro-scale tissue volumes at sub-micrometer resolution while providing a data rate approaching the transfer speed of most modern computer systems. Since receiving his Ph.D., Dr. Mayerich worked as a postdoctoral fellow at the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign, where he has worked with biologists and biomedical engineers to develop tools for the segmentation and classification of large data sets. This provided experience in addressing the needs and limitations of the computational tools available to the interdisciplinary community. The goal of the mentored phase of this proposal is to provide Dr. Mayerich with the opportunity to work as a developer for the FARSIGHT Toolkit. The FARSIGHT Toolkit is an open-source segmentation toolkit that focuses on developing computer vision algorithms specifically tailored to deal with the unique structures found in microscopy data sets. This project is directed by Prof. Badrinath Roysam at the University of Houston, and was awarded first-place in the NIH-sponsored DIADEM Challenge in neuron segmentation. Dr. Mayerich will use his previous experience in biomedical segmentation, GPU-based computing, and efficient data structures to help make the FARSIGHT Toolkit scalable to the terabyte-scale data sets produced using next-generation high-throughput imaging techniques. Dr. Mayerich will receive mentoring in the algorithms and techniques used in the FARSIGHT Toolkit, as well as valuable experience working on a collaborative software development project. The goal of the independent phase is to use recently developed imaging techniques, along with scalable segmentation algorithms, to construct complete microvascular models of mouse organs. Recent advances in KESM demonstrate that sub-micrometer images of 1cm3 tissue samples can be collected in less than 50 hours. These images have the resolution and quality necessary for (a) complete reconstruction of microvascular networks in whole organs, and (b) the geometric distribution of cell soma in relation to this network. Models describing cellular and microvascular relationships have implications in several diseases, including neurodegenerative disease and tumor growth, as well as clinical applications in tissue engineering and the quantitative analysis of angiogenic drugs and therapies.
The goal of this work is to produce high-resolution microvascular models from mouse brain tissue, as well as create algorithms for querying, distributing, and building models from next-generation high-throughput microscopy data sets. These techniques will allow researchers to create large-scale blood flow simulations, simulate the extent of tissue damage due to stroke or aneurism, and explore the relationships between cells and microvessels on a tissue-wide scale. Clinical applications include the quantification of angiogenesis in tumors and tissue implants, and the quantification of neurovascular effects in neurodegenerative disease models.