In vivo digital retinal pathology: imaging retina with molecular specific contrast and cellular resolution

体内数字视网膜病理学：利用分子特异性对比度和细胞分辨率对视网膜进行成像

• 批准号: RGPIN-2020-06860
• 负责人: JU, MYEONGJIN
• 金额: $ 2.04万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761870
• 关键词: vivo; digital; retinal; pathology; imaging

The overarching goal of my research program is to develop novel imaging tools and technology for in vivo retinal imaging which will enable dynamic cellular and molecular analysis of disease mechanism in living animal models. These advanced bio-microscopic tools have potential to contribute not only to the development of regenerative medicine and stem cell therapies, but also to generate novel concepts for high resolution microscopy. Objective 1. The principle aim of my research program is to develop and advance Visible light All-in-One (VAiO) retinal imaging technology that comprises a retinal Optical Coherence Tomography (OCT) system with visible light illumination, Adaptive Optics (AO), and Polarization Diversity Detection (PDD). Using VAiO-OCT the morphological detail of individual retinal layers will be visualized with multiple optical contrasts at a 3D isotropic cellular resolution to enable non-invasive dynamic retinal molecular imaging capabilities. Objective 2. A complementary second objective of my research program is to develop novel computational image analysis and machine learning tools that are tailored specifically for digital retinal pathology. By clustering pixels based on their spatial proximity and multiple optical feature similarity, a digital `stain' for retinal tissues from VAiO-OCT image can be achieved, which would be useful for highlighting different tissue groups inside of the retina and enhance their contrast. This objective will achieve predictive modeling of retinal pathology images from a detection, segmentation, feature extraction, and tissue classification perspective.

我的研究计划的总体目标是开发新的成像工具和技术，在体内视网膜成像，这将使动态细胞和分子分析疾病的机制，在活体动物模型。这些先进的生物显微镜工具不仅有助于再生医学和干细胞治疗的发展，而且还为高分辨率显微镜产生了新的概念。目的1。我的研究计划的主要目的是开发和推进可见光一体化（VAiO）视网膜成像技术，该技术包括具有可见光照明的视网膜光学相干断层扫描（OCT）系统，自适应光学（AO）和偏振分集检测（PDD）。使用VAiO-OCT，单个视网膜层的形态细节将在三维各向同性细胞分辨率下通过多重光学对比度可视化，以实现非侵入性动态视网膜分子成像能力。目标2。我研究计划的第二个补充目标是开发专门为数字视网膜病理学量身定制的新型计算图像分析和机器学习工具。通过基于空间接近度和多个光学特征相似性的像素聚类，可以实现VAiO-OCT图像中视网膜组织的数字“染色”，这将有助于突出显示视网膜内不同的组织组并增强它们的对比度。该目标将从检测、分割、特征提取和组织分类的角度实现视网膜病理图像的预测建模。