The molecular biophotonics lab develops optical-imaging solutions for improved disease management.  In particular, we are developing custom optical-sectioning (3D) microscopy devices and machine-learning analysis strategies for early cancer detection, improved diagnosis/grading of tumors, and surgical guidance.  These projects leverage our multidisciplinary expertise in biomedical optics, machine learning, spectroscopy, molecular imaging, mechanical and electrical instrumentation, contrast agents, and preclinical/clinical translation. 

See links on the left for additional details

This image shows a 3D pathology dataset of a prostate biopsy stained with a fluorescent analogue of H&E (left). We perform deep learning-based image translation to convert the H&E dataset into a synthetic dataset that looks like it has been immunolabeled to highlight a cytokeratin biomarker (brown) that is expressed by the epithelial cells in all prostate glands. In turn, this synthetically immunolabeled dataset allows for accurate 3D segmentation of the prostate gland epithelium (yellow) and lumen spaces (red). Quantitative features derived from these segmented 3D structures are used to train a machine classifier to stratify between aggressive (recurrent) versus indolent (non-recurrent) cancer.  [W. Xie et al., Cancer Research, 2022]