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放射成像。现代放射学和成像科学的一个主要目标是利用专门的生物物理模型来模拟活组织中的生物过程，以产生用于疾病检测的灵敏成像对比度。为了了解模拟图像对比度与潜在的病理生理学之间的关系，必须进行放射病理图像分析，以验证图像在组织结构、病理和疾病特征方面的相关性。考虑到组织中复杂的微环境，放射学图像和病理图像的比较尤其具有挑战性。实验室中的许多常规分析在很大程度上依赖于使用为一般目的设计的商业软件对“黄金标准”病理图像中的细胞和组织进行手动或半自动计数和分割。研究人员经常不得不放弃在病理图像中显示的大量信息，但这些信息无法使用现有方法进行量化。这个项目旨在通过利用深度学习方法来提取放射和病理图像中的重要特征，以定量分析以前在社区中无法获得的相关性，从而缩小差距。为了解决一直存在的比较放射和病理图像的挑战，该项目的技术目标分为三个方面：(1)用于量化整个大脑切片的细胞形态表型的深度学习算法；(2)用于可视化放射与病理图像相关性分析的图形和交互统计工具箱；(3)在用户友好的平台上实现计算机辅助图像分析和放射与病理相关性数据库的网站即服务软件包。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记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，通过在生物成像研究领域使用尖端人工智能，支持生物和数学专业的多样化和代表性不足的学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.