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

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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.