STTR Phase II: Deep Learning Technology For The Microscopic Analysis Of Stained Cells Using Unbiased Methods

这个小企业技术转让（STTR）第二阶段项目的更广泛的影响/商业潜力是开发软件，以帮助生物科学家在更短的时间内分析更多的组织，并具有更高的准确性和再现性。 目前，体视学研究需要训练有素的技术人员坐在计算机屏幕前，对数百到数千个微观细胞进行繁琐的手动计数（点击）。正在开发的技术使用一系列算法，将联合收割机无偏体视学与强大的新方法-深度学习（人工智能）-结合起来，以比现有方法更客观、更快、更可重复的方式量化染色细胞的体视学变化。 使用客观的深度学习方法自动化体视学的一个主要优点是节省了训练有素的技术人员收集数据所需的时间和成本。这项技术将加快对人类疾病原因的研究步伐，并加速开发新的治疗策略。 生物科学家和医学科学家将更好地服务于社会的需要，更好地了解健康，衰老和疾病中的细胞结构，而该技术将确保准确和精确的体视学结果，而不需要经过高度训练的数据收集者，并且在现有方法的一小部分时间和成本上。生物医学和生物科学研究。 第一阶段研究的结果表明，组织切片的自动计数速度比手动体视学快10倍以上，精度与手动体视学相同。 此外，再现性为99%，并且几乎不需要或不需要用户培训。 第二阶段的目标是开发标准化、高通量的深度学习网络，用于量化染色细胞和组织的其他体视学参数，验证主动深度学习作为一种为所有用户染色方案定制深度学习的技术，并更新软件和文档以支持用户友好的工作流程。其目的是训练神经网络，以准确地分割具有可变形态的更广泛的细胞和组织病理，其性能指标的准确度为95%。 无偏体视学将使生物科学家能够准确地量化导致代谢疾病，神经系统疾病，毒性反应和精神疾病的细胞变化。 自动化收集体视学数据的过程将加速科学研究、毒理学研究和药物开发，并通过科学突破和医学发现为社会带来重大利益。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is the development of software to help bioscientists analyze more tissue in less time and with higher accuracy and reproducibility. Currently, stereology studies require a trained technician to sit before a computer screen, making tedious manual counts (clicks) on hundreds to thousands of microscopic cells. The technology under development uses a series of algorithms that combine unbiased stereology with a powerful new approach - deep learning (artificial intelligence) - to quantify stereology changes in stained cells in an objective, faster and more reproducible manner than existing methods. A major advantage of automating stereology using an objective deep learning approach is the savings in time and costs currently required of a trained technician to collect data. This technology will improve the pace of research into the causes of human diseases and accelerate the development of novel strategies for the therapeutic management of afflicted patients. Bioscientists and medical scientists will better serve the needs of society for a greater understanding of cellular structure in health, aging and disease, while the technology will ensure accurate and precise stereology results without the need for highly trained data collectors and at a small fraction of the time and cost of existing approaches.This STTR Phase II project proposes to develop software to automate the collection of stereology data in biomedical and bioscience research. The results of the Phase I research showed that tissue sections were automatically counted over 10X times faster with equal accuracy as manual stereology. In addition, reproducibility was 99% and required little or no user training. The Phase II objectives are to develop standardized, high-throughput, deep learning networks for quantifying other stereology parameters of stained cells and tissues, validate active deep learning as a technique for customizing deep learning for all user staining protocols, and update the software and documentation to support user-friendly workflow. The aim is to train the neural network to accurately segment a wider range of cell and tissue pathology with variable morphologies with a performance metric for accuracy of 95%. Unbiased stereology will allow bioscientists to accurately quantify the cellular changes that cause metabolic diseases, neurological disorders, toxic reactions and mental illnesses. Automating the process of collecting stereology data will accelerate scientific research, toxicology studies and drug development, and bring significant benefits to society through scientific breakthroughs and medical discoveries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.