NSF EAGER: DEEP LEARNING-BASED VIRTUAL HISTOLOGY STAINING OF TISSUE SAMPLES

NSF EAGER：基于深度学习的组织样本虚拟组织学染色

• 批准号: 1926371
• 负责人: Aydogan Ozcan
• 金额: $ 30万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926371&HistoricalAwards=false
• 关键词: NSF; EAGER; DEEP; LEARNING; BASED

Microscopic imaging of tissue samples is a fundamental tool used for the diagnosis of various diseases and forms the workhorse of pathology and biological sciences. The clinically-established gold standard image of a tissue section is the result of a laborious process. This work will demonstrate the ability to virtually stain label-free tissue sections and will revolutionize the current paradigm for histological analysis.To demonstrate deep learning-based virtual histology staining of label-free human tissue samples this proposal will use salivary gland, thyroid, kidney, liver and lung samples, and will use three commonly used stains: H&E (salivary gland and thyroid), Jones stain (kidney) and Masson's Trichrome (liver and lung). This proposal will determine the staining efficacy of the proposed approach for whole slide images and will blindly evaluate the virtually stained outputs with gold standard stained samples. The output of this proposed system will be validated by a group of pathologists who will compare histopathological features with the virtual staining technique against conventional histology techniques.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.

组织样本的显微成像是用于诊断各种疾病的基本工具，并且形成病理学和生物科学的主力。临床上建立的组织切片的金标准图像是费力过程的结果。这项工作将展示对无标记组织切片进行虚拟染色的能力，并将彻底改变当前的组织学分析范式。为了展示基于深度学习的无标记人体组织样本的虚拟组织学染色，该提案将使用唾液腺、甲状腺、肾脏、肝脏和肺样本，并将使用三种常用的染色剂：HE（唾液腺和甲状腺）、Jones染色剂（肾脏）和Masson三色（肝脏和肺）。本提案将确定所提出的方法对整个载玻片图像的染色效果，并将使用金标准染色样本对虚拟染色输出进行盲态评价。该系统的输出将由一组病理学家进行验证，他们将比较虚拟染色技术与传统组织学技术的组织病理学特征。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

Virtual Immunohistochemical Staining of Label-free Breast Tissue Using Deep Learning

使用深度学习对无标记乳腺组织进行虚拟免疫组织化学染色

• 发表时间: 2022
• 期刊: BMES (Biomedical Engineering Society
• 作者: B. Bai, H. Wang

Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

• DOI: 10.1038/s41377-020-0315-y
• 发表时间: 2020-05-06
• 期刊: LIGHT-SCIENCE & APPLICATIONS
• 影响因子: 19.4
• 作者: Zhang, Yijie;de Haan, Kevin;Ozcan, Aydogan
• 通讯作者: Ozcan, Aydogan

Deep learning-based transformation of H&E stained tissue into special stains

基于深度学习的H变换

• DOI: 10.1364/cleo_at.2022.ath2i.4
• 发表时间: 2022
• 期刊: Optica Conference on Lasers and Electro-Optics (CLEO
• 作者: de Haan, Kevin;Zhang, Yijie;Zuckerman, Jonathan E.;Liu, Tairan;Rivenson, Yair;Wallace, W. Dean;Ozcan, Aydogan
• 通讯作者: Ozcan, Aydogan

Virtual stain transfer in histology via cascaded deep neural networks

• DOI: 10.1021/acsphotonics.2c00932
• 发表时间: 2022-07
• 期刊: ArXiv
• 作者: Xilin Yang;Bijie Bai;Yijie Zhang;Yuzhu Li;K. Haan;Tairan Liu;Aydogan Ozcan

Virtual tissue staining in pathology using machine learning

使用机器学习进行病理学虚拟组织染色

• DOI: 10.1080/14737159.2022.2153040
• 发表时间: 2022
• 期刊: Expert Review of Molecular Diagnostics
• 影响因子: 5.1
• 作者: Pillar, Nir;Ozcan, Aydogan
• 通讯作者: Ozcan, Aydogan