CAREER: Computational structured light imaging for widefield mapping of histologic primitives

Johns Hopkins University, Baltimore MD

Investigators

Abstract

The research goal of this CAREER project is to create new optical and computational tools to improve histological imaging of unprocessed tissues. Histology is essential in the diagnosis and treatment of most cancers, yet its core workflow has not changed in over a century. This research explores the potential of a patterned ultraviolet light and artificial intelligence approach to directly image histological information over large areas. This “slide-free” approach could increase speed and accuracy of diagnosis while improving surgery and assessment for a wide range of cancers. This research plan is closely integrated with an educational program that aims to train undergraduate engineers to solve important clinical needs in pathology, connect pathologists with engineers through large hackathons, and introduce high school students to medical device design. Conventional spatial frequency domain imaging (SFDI) provides quantitative optical property maps of large tissues but suffers from poor resolution and optical sectioning due to decreasing modulation depths with increasing spatial frequencies. This program will create a novel macroscope that projects ultraviolet illumination at very high spatial frequencies to recover high-resolution, superficial optical property maps of bulk tissues. Histologic primitive maps that quantify nuclei morphology, cellularity, and other clinically relevant features, will be measured via Microscopy by Ultraviolet Excitation (MUSE). Paired optical property and histologic primitive maps of ex-vivo human skin samples will be acquired for training and testing machine learning models. New multimodal deep learning architectures will be researched to predict these histologic primitives directly from macroscopy inputs and also to jointly optimize the macroscope hardware and prediction algorithm. The knowledge gained from this research will be important for 1) bridging the knowledge gap between diffuse optical imaging and microscopy, 2) understanding how to co-design artificial intelligence models with imaging hardware, and 3) creating a foundation for innovative histology tools that could dramatically improve pathology workflows. 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.