Catalyst Project: Quantification of immunohistochemistry images of neuroglia

催化剂项目：神经胶质细胞免疫组织化学图像的量化

• 批准号: 2200489
• 负责人: Tsang-Wei Tu
• 金额: $ 15万
• 依托单位: Howard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200489&HistoricalAwards=false
• 关键词: Catalyst; Project; Quantification; immunohistochemistry; images

Catalyst Projects provide support for Historically Black Colleges and Universities (HBCU) to work towards establishing research capacity of faculty to strengthen science, technology, engineering, and mathematics (STEM) undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution, and involve undergraduate students in research experiences. This award to Howard University supports the use of artificial intelligence and machine learning to develop improved technology for the unbiased, high throughput morphological detection and quantification of glial cells. It is anticipated that the proposed model will overcome the limitations existing methodologies, and the new deep learning system will reveal previously uncharacterized yet fundamental limitations in high-resolution segmentation of neuroglia cells.Development of a novel deep learning system is proposed to resolve the difficulties in quantifying region-specific morphology and the cell subcategories in brain pathology. The proposal aims to characterize the hallmark features within glial cells, include the some and extended glial processes, which are known pathological determinants of glial cells. The approach involves a composite deep learning system with a convolutional neural network for cell detection, followed by a dedicated segmentation classifier for single cell segmentation to identify the heterogeneous glial cells with appearance of region-specific features in the background. Morphological parameters will be determined and used to predict the activation phenotypes of glial cells based on the most widely used 2D, 20X immunohistochemistry images. To enhance the model performance, a user-friendly web toolbox will be developed for fast data curation and integrated into a comprehensive database of the annotated cell morphology and phenotypes for model training and testing. The proposed model will be compared to the gold-standard manual data curated by pathology experts, and other existing computer-aided methodologies, including the rule-based semi-automatic methods, and deep learning-based methods to control for effectiveness, consistency, and computational efficiency. The automated analysis, feature-rich visualization, database integration and open-source distribution with easy access from the community, will allow this system to become a routine workflow for glial image analysis. This new technological will benefit the neuroscience research community by providing a fully automated tool for quantitative histology.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.

催化剂项目为历史上的黑人学院和大学（HBCU）提供支持，致力于建立教师的研究能力，以加强科学，技术，工程和数学（STEM）本科教育和研究。预计该奖项将进一步提高教师的研究能力，改善研究和教学机构，并参与本科生的研究经验。授予霍华德大学的这一奖项支持使用人工智能和机器学习来开发改进的技术，用于无偏、高通量的形态学检测和定量神经胶质细胞。预计提出的模型将克服现有方法的局限性，新的深度学习系统将揭示以前未表征的神经胶质细胞的高分辨率分割的基本局限性，提出了一种新的深度学习系统的开发，以解决在量化特定区域的形态和脑病理中的细胞亚类的困难。该提案旨在表征神经胶质细胞内的标志性特征，包括一些和扩展的神经胶质过程，这是已知的神经胶质细胞的病理决定因素。该方法涉及一个复合深度学习系统，该系统具有用于细胞检测的卷积神经网络，然后是用于单细胞分割的专用分割分类器，以识别背景中出现区域特定特征的异质神经胶质细胞。基于最广泛使用的2D、20X免疫组织化学图像，将确定形态学参数并用于预测神经胶质细胞的活化表型。为了提高模型性能，将开发一个用户友好的网络工具箱，用于快速数据管理，并将其整合到一个注释细胞形态和表型的综合数据库中，用于模型训练和测试。该模型将与病理学专家管理的黄金标准手动数据以及其他现有的计算机辅助方法进行比较，包括基于规则的半自动方法和基于深度学习的方法，以控制有效性，一致性和计算效率。自动化分析，功能丰富的可视化，数据库集成和开源分发，易于从社区访问，将使该系统成为胶质图像分析的常规工作流程。这项新技术将为神经科学研究界提供一个全自动的定量组织学工具。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。

项目成果

Classification of Activated Microglia by Convolutional Neural Networks

卷积神经网络对激活的小胶质细胞进行分类

• DOI: 10.1109/biocas54905.2022.9948635
• 发表时间: 2022
• 期刊: 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS
• 作者: Hsu, Chao-Hsiung;Agaronyan, Artur;Katherine, Raffensperger;Kadden, Micah;Ton, Hoai T.;Wu, Frank;Lin, Yu-Shun;Lee, Yih-Jing;Wang, Paul C.;Shoykhet, Michael
• 通讯作者: Shoykhet, Michael