Excellence in Research: PathoRadi ‒ an interactive web server for AI-assisted radiologic-pathologic image analysis, correlation and visualization

放射成像是医疗保健服务的重要组成部分，医生在医疗决策过程中严重依赖于它。现代放射学和成像科学的主要目标是利用专门的生物物理建模，其模拟活组织中的生物过程以生成用于疾病检测的灵敏成像对比度。为了理解模拟图像对比度与潜在病理生理之间的关系，必须执行放射病理图像分析以验证组织结构、病理和疾病特征中的图像相关性。鉴于组织中复杂的微环境，放射学和病理学图像的比较特别具有挑战性。实验室中的许多常规分析在很大程度上依赖于使用为通用目的设计的市售软件对“金标准”病理图像中的细胞和组织进行手动或半自动计数和分割。研究人员往往不得不放弃大量的信息，显示在病理图像，但不能量化使用现有的方法。该项目旨在通过利用深度学习方法来提取放射学和病理学图像中的重要特征，以定量分析以前在社区中无法实现的相关性，从而缩小差距。为了应对在比较放射学和病理学图像方面持续存在的挑战，该项目的技术目标分为三个方面：（1）用于量化全脑切片中的细胞形态表型的深度学习算法，（2）用于可视化放射学-病理学图像相关性分析的图形和交互式统计工具箱，（3）一个网站即服务软件包，在一个用户友好的平台上实现计算机辅助图像分析和数据库，用于放射病理学相关性。该项目成果提供了一种新的深度学习方法，可用于标准化放射成像生物标志物开发中的基准评估。该奖项加强了霍华德大学的研究生和本科生STEM教育，支持生物学和数学专业多样化和代表性不足的学生群体，通过切割-该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

Radiological imaging is a critical part of healthcare services which physicians rely heavily upon in the medical decision-making process. A major goal of modern radiology and imaging sciences is to exploit specialized biophysical modeling that simulates the biological process in the living tissue to generate sensitive imaging contrast for disease detection. In order to understand the relations between the simulated image contrast and the underlying pathophysiology, radiologic-pathologic image analysis has to be performed to validate the image correlations in tissue structure, pathology and disease characteristics. Given the complex microenvironments in the tissues, comparison of radiologic and pathologic images is particularly challenging. Many of the routine analyses in the laboratories largely depend on manual or semi-automatic counting and segmentation of cells and tissues in the “gold standard” pathological images using commercially available software that are designed for general purposes. Researchers often have to give up an ample amount of information that shows in the pathological images but not quantifiable using the existing methods. This project aims to close the gap by utilizing deep learning methodology to extract the important features in the radiological and pathological images for quantitative analysis of the correlations previously unattainable in the community. To address the challenges that persist in comparing radiologic and pathologic images, the technical aims of the project are divided into three aspects: (1) deep learning algorithms for quantifying cell morphological phenotypes in the whole brain sections, (2) a graphical and interactive statistic toolbox to visualize the radiologic-pathologic image correlation analysis, (3) a website-as-a-service software package that implements computer-aided image analysis and database for radiologic-pathologic correlations in a user-friendly platform. The project outcome provides a novel deep learning methodology that can be used to standardize the benchmark evaluations in the development of radiological imaging biomarkers. The award enhances the graduate and undergraduate STEM education at the Howard University, with supports to a diverse and underrepresented cohort of the students in the biology and mathematics majors, through the use of cutting-edge artificial intelligence in the field of bioimaging research.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.