STTR Phase I: Microscope-based Technology For Automatic Brain Cell Counts Using Unbiased Methods

STTR 第一阶段：基于显微镜的技术，使用无偏差方法进行自动脑细胞计数

• 批准号: 1746511
• 负责人: Peter Mouton
• 金额: $ 22.44万
• 依托单位: Stereology Resource Center, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1746511&HistoricalAwards=false
• 关键词: STTR; Phase; Microscope; based; Technology

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 This outdated approach fails to take advantage of powerful deep learning methods proposed for the proposed software that could complete these tasks 10 times faster and with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.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力是自动化无偏体视学过程，这是生命科学中用于计数组织切片上染色细胞的方法。 无偏见的体视学使神经科学家能够准确地分析脑细胞的大小和数量，这些细胞在许多神经系统疾病和精神疾病中发生了变化。由于目前未知的原因，阿尔茨海默病，帕金森病和肌萎缩性侧索硬化症都与脑细胞的进行性损失有关。相比之下，自闭症儿童天生就有太多的脑细胞，这导致他们在处理复杂的信息流方面存在终身问题。 体视学在研究影响大脑的许多疾病和评估可能治疗的有效性和安全性方面发挥着重要作用。虽然这项技术最初将以研究为目标，以了解和治疗所有神经系统疾病，但它可以用于自动评估所有组织中的细胞，包括癌症筛查和活检诊断。该项目将开发和优化一种算法，以帮助脑科学家确定神经系统疾病和精神疾病的原因和治疗方法。拟议中的技术将使用深度学习（人工智能）系统来自动识别、计数和确定组织切片上的脑细胞大小。这项技术的一个重要用途是分析小鼠和大鼠的大脑和神经组织，这些小鼠和大鼠被治疗后表现出与人类相似的神经系统疾病。这些动物模型为测试治疗人类脑部疾病的治疗方法提供了强大的工具。目前，对这些动物组织的体视学研究需要训练有素的技术人员坐在电脑屏幕前，对成百上千的微观细胞进行繁琐的人工计数。这种过时的方法未能利用为拟议软件提出的强大的深度学习方法，这些方法可以以10倍的速度完成这些任务，并且错误和人为偏见更少。因此，拟议中的技术将利用深度学习技术来加速美国的基础研究和药物开发，从而建立一个长期的经济引擎，并通过科学突破和医学发现为社会带来重大利益。

项目成果

Unbiased estimation of cell number using the automatic optical fractionator.

• DOI: 10.1016/j.jchemneu.2016.12.002
• 发表时间: 2017-03
• 期刊: Journal of chemical neuroanatomy
• 影响因子: 2.8
• 作者: Mouton PR;Phoulady HA;Goldgof D;Hall LO;Gordon M;Morgan D
• 通讯作者: Morgan D

Automatic ground truth for deep learning stereology of immunostained neurons and microglia in mouse neocortex

• DOI: 10.1016/j.jchemneu.2019.02.006
• 发表时间: 2019-03
• 期刊: Journal of Chemical Neuroanatomy
• 影响因子: 2.8
• 作者: H. A. Phoulady;Dmitry Goldgof;L. Hall;P. Mouton

Automatic stereology of mean nuclear size of neurons using an active contour framework

使用活动轮廓框架自动测量神经元平均核大小

• DOI: 10.1016/j.jchemneu.2018.12.012
• 发表时间: 2019
• 期刊: Journal of Chemical Neuroanatomy
• 影响因子: 2.8
• 作者: Ahmady Phoulady, Hady;Goldgof, Dmitry;Hall, Lawrence O.;Nash, Kevin R.;Mouton, Peter R.
• 通讯作者: Mouton, Peter R.

Automated Cell Counts on Tissue Sections by Deep Learning and Unbiased Stereology

• DOI: 10.1016/j.jchemneu.2018.12.010
• 发表时间: 2019-03-01
• 期刊: JOURNAL OF CHEMICAL NEUROANATOMY
• 影响因子: 2.8
• 作者: Alahmari, Saeed S.;Goldgof, Dmitry;Mouton, Peter R.
• 通讯作者: Mouton, Peter R.