CRII:SCH:A Generative Deep Learning (GDL) based Platform for Super-resolution, Virtual-Pathological Visualization of Coronary Images

CRII：SCH：基于生成深度学习（GDL）的平台，用于冠状动脉图像的超分辨率、虚拟病理可视化

• 批准号: 1948540
• 负责人: Yu Gan
• 金额: $ 17.5万
• 依托单位: University of Alabama Tuscaloosa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948540&HistoricalAwards=false
• 关键词: CRII; SCH; Generative; Deep; Learning

Coronary artery disease (CAD) has been influencing a market over $2.8 billion with roughly 1,000,000 treatment procedures performed annually. Existing guidance for CAD treatment suffers from limited spatial resolution and lacks real-time detailed pathological identification. This project is to investigate novel computational techniques for pathological, super-resolution visualization of coronary images. The project, if successful, will contribute towards a new generation of clinical guidance for the treatment of cardiovascular disease, which is currently the leading cause of human deaths in the United States. Technically, the method of encoding multi-domain image representations into a single-domain image acquisition could benefit other fields, such as multi-camera surveillance monitoring, multimodal biomedical imaging, etc., in terms of greatly reducing hardware cost and medical labor. The educational plan in this project emphasizes activities designed to guide senior designs, enrich curriculum in deep learning courses, and facilitate outreach for minority students in multicultural engineering programs.This project aims to develop a data-driven approach to use off-line data and training process, without any hardware modifications, to generatively produce new information that could not be acquired previously or can only be obtained ex vivo. This project will develop generative deep learning algorithms to produce additional information for low-resolution optical coherence tomography (OCT) images by aggregating image information from high-resolution OCT images and histological microscopic images during off-line training. This project will investigate on improving the resolution of OCT while maintaining fast scanning rate via a volumetric generative adversarial network (GAN) for super-resolution. This project will develop a novel unpaired training scheme to map OCT image to a histopathology image by using a GAN-based image translation framework. The approach will be validated using both objective and subjective analysis on OCT images and histopathology images. This project is expected to generate academic outcomes in both computer science and biomedical informatics. This project will provide a deep learning solution for cross-platform volumetric super-resolution and a generative learning approach to address cross-modality image translation.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.

冠状动脉疾病（CAD）已经影响了一个超过28亿美元的市场，每年大约有100万例治疗程序。现有的CAD治疗指南空间分辨率有限，缺乏实时详细的病理识别。本项目旨在研究冠状动脉图像病理超分辨率可视化的新计算技术。该项目如果成功，将有助于为治疗心血管疾病提供新一代临床指导，心血管疾病目前是美国人类死亡的主要原因。从技术上讲，将多域图像表示编码为单域图像采集的方法可以大大降低硬件成本和医疗人力，从而有利于其他领域，如多摄像机监控、多模态生物医学成像等。本项目的教育计划强调指导高年级设计的活动，丰富深度学习课程的课程，促进少数民族学生在多元文化工程项目中的推广。该项目旨在开发一种数据驱动的方法，利用离线数据和训练过程，不需要任何硬件修改，生成以前无法获得或只能在体外获得的新信息。该项目将开发生成式深度学习算法，通过在离线训练期间聚合高分辨率OCT图像和组织学显微图像的图像信息，为低分辨率光学相干断层扫描（OCT）图像生成额外信息。本项目将研究如何通过体积生成对抗网络（GAN）来提高OCT的分辨率，同时保持快速扫描速率。本项目将开发一种新的非配对训练方案，通过使用基于gan的图像翻译框架将OCT图像映射到组织病理学图像。该方法将通过对OCT图像和组织病理学图像的客观和主观分析来验证。该项目有望在计算机科学和生物医学信息学方面产生学术成果。该项目将为跨平台体积超分辨率提供深度学习解决方案，并为解决跨模态图像翻译提供生成学习方法。该奖项反映了美国国家科学基金会的法定使命，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

Inpainting for Saturation Artifacts in Optical Coherence Tomography Using Dictionary-Based Sparse Representation

• DOI: 10.1109/jphot.2021.3056574
• 发表时间: 2021-04-01
• 期刊: IEEE PHOTONICS JOURNAL
• 影响因子: 2.4
• 作者: Liu, Hongshan;Cao, Shengting;Gan, Yu
• 通讯作者: Gan, Yu

Co-Seg: An Image Segmentation Framework Against Label Corruption

• DOI: 10.1109/isbi48211.2021.9433790
• 发表时间: 2021-01
• 期刊: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
• 作者: Ziyi Huang;Haofeng Zhang;A. Laine;E. Angelini;C. Hendon;Yu Gan