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STTR Phase I: Microscope-based Technology For Automatic Brain Cell Counts Using Unbiased Methods
Phone: (410) 490-2034
Phone: (410) 490-2034
Contact: Dmitry Goldgof
Type: Nonprofit College or University
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is in automating the process of unbiased stereology, the state-of-the-method used in the life sciences for counting stained cells on tissue sections. Unbiased stereology allows neuroscientists to accurately analyze the size and number of brain cells, which are altered in many neurological disorders and mental illnesses. For reasons that are currently unknown, Alzheimer's disease, Parkinson's disease and Amyotrophic Lateral Sclerosis are all associated with a progressive loss of brain cells. In contrast, children with autism are born with too many brain cells, which leads to life-long problems in processing complex streams of information. Stereology plays an important role in investigating many conditions affecting the brain and assessing the efficacy and safety of possible treatments. Though the proposed technology will initially target research studies to understand and treat all neurological conditions, it can be useful for automatic assessments of cells in all tissues, including cancer screening and diagnosis from biopsies. The proposed project will develop and optimize an algorithm to help brain scientists identify causes and treatments for neurological disease and mental illness. The proposed technology will use deep learning (artificial intelligence) systems to automatically recognize, count and size brain cells on tissue sections. An important use of this technology will be to analyze brains and nerve tissue from mice and rats treated to show similar neurological diseases as those found in humans. These animal models provide a powerful tool for testing treatments to cure brain disease in humans. Currently stereology studies of tissues from these animals require a trained technician to sit before a computer screen making tedious manual counts of hundreds and thousands of microscopic cells. This outdated approach fails to take advantage of powerful deep learning methods proposed for the proposed software that could complete these tasks 10 times faster and with fewer errors and human biases. Therefore, the proposed technology will use deep learning technology to accelerate basic research and drug development in the U.S., thereby establishing a long-term economic engine and bringing significant benefits to society through scientific breakthroughs and medical discoveries.
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