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

基本信息

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

项目摘要

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）一直在影响超过28亿美元的市场，每年进行约1,000,000个治疗程序。现有的CAD治疗指南遭受有限的空间分辨率，缺乏实时详细的病理鉴定。该项目是为了研究用于冠状动脉图像的病理，超分辨率可视化的新型计算技术。如果成功的话，该项目将有助于新一代的临床指导，以治疗心血管疾病，这是美国人类死亡的主要原因。从技术上讲，将多域图像表示形式编码为单域图像采集可以使其他领域受益，例如多模式监视，多模式生物医学成像等，大大降低硬件成本和医疗劳动。该项目中的教育计划强调了旨在指导高级设计，丰富的深度学习课程课程的活动，并促进了在多元文化工程计划中为少数族裔学生提供外展活动。该项目旨在开发一种使用数据驱动的方法来使用离线数据和培训过程，而无需任何硬件修改，以至于无法获得任何新的信息，以前无法获得任何硬件的信息，或者只能获得Exex viv。该项目将开发生成的深度学习算法，以通过在离线训练期间从高分辨率OCT图像和组织学显微镜图像中汇总图像信息，从而为低分辨率光学相干断层扫描（OCT）图像提供其他信息。该项目将调查有关通过体积生成对抗网络（GAN）维持快速扫描速率改善OCT的分辨率的研究。该项目将通过使用基于GAN的图像翻译框架来开发一种新颖的未配对训练方案，以将OCT图像映射到组织病理学图像。该方法将使用OCT图像和组织病理学图像的客观和主观分析来验证。预计该项目将在计算机科学和生物医学信息学上产生学术成果。该项目将为跨平台大量超级分辨率和一种生成学习方法提供深入学习解决方案，以解决交叉模式图像翻译。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子的智力优点和更广泛的影响标准通过评估来获得支持。