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.

组织样品的微观成像是一种基本工具，用于诊断各种疾病，形成病理学和生物科学的主力。组织部分的临床建立的黄金标准图像是一个费力的过程。 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 （肾脏）和Masson的三色（肝脏和肺）。该提案将确定整个幻灯片图像的拟议方法的染色功效，并将用金标准染色样品盲目评估几乎染色的输出。该提出的系统的输出将由一组病理学家验证，他们将将组织病理学特征与针对常规组织学技术进行虚拟染色技术进行比较。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来进行评估的。

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

Neural network-based multiplexed and micro-structured virtual staining of unlabeled tissue

基于神经网络的未标记组织的多重和微结构虚拟染色

• DOI: 10.1364/cleo_at.2022.ath2i.2
• 发表时间: 2022
• 期刊: Optica Conference on Lasers and Electro-Optics (CLEO
• 作者: Zhang, Yijie;de Haan, Kevin;Li, Jingxi;Rivenson, Yair;Ozcan, Aydogan
• 通讯作者: Ozcan, Aydogan