Building 10, Room 10N226 10 Center Drive, Bethesda, MD 20892-1860
The goal of our research is to understand the onset and progression of retinal diseases at the cellular level, using advanced optical imaging techniques such as adaptive optics.
Adaptive optics is a technology for measuring and correcting the optical imperfections utilized in astronomy, microscopy, and vision science. When combined with a state-of-the-art ophthalmic imaging platform, highly detailed images of the cells in the human retina can be acquired.
The approach is to visualize healthy and diseased cells directly inside patients’ eyes to determine the sequence and timing of all the cumulative microscopic changes that give rise to clinically-significant disease phenotypes. Our research spans the development, implementation, and application of advanced optical instrumentation, as well as the acquisition, processing, and analysis of rich imaging datasets. We are particularly interested in studying the outer retina, consisting of photoreceptor neurons, retinal pigment epithelial cells, and choriocapillaris blood vessels. This multi-layered complex is not only critical for the phenomenon of vision, but also, is a useful system for modeling the in vivo interactions of neurons, epithelial cells, and vasculature within the central nervous system, in health, aging, and disease.
This unit is part of the Ophthalmic Genetics and Visual Function Branch.
J. Liu, H. Jung, J. Tam, “Accurate Correspondence of Cone Photoreceptor Neurons in the Human Eye Using Graph Matching Applied to Longitudinal Adaptive Optics Images,” Medical Image Computing and Computer-Assisted Intervention – MICCAI 2017, Lecture Notes in Computer Science, Vol 10434, Springer
T. Liu, H. Jung, J. Liu, M. Droettboom, and J. Tam, “Noninvasive near infrared autofluorescence imaging of retinal pigment epithelial cells in the human retina using adaptive optics,” Biomedical Optics Express 8(10):4348-4360, 2017
J. Liu, H. Jung, A. Dubra, and J. Tam, “Automated Photoreceptor Cell Identification on Non-Confocal Adaptive Optics Images Using Multi-Scale Circular Voting,” Investigative Ophthalmology and Visual Science 58(11):4477-4489, 2017
J. Tam, M. Droettboom, J. Liu, and H. Jung, “Noninvasive infrared autofluorescence imaging of intrinsic fluorophores in the human retina at cellular-level resolution using adaptive optics,” Optics in the Life Sciences 2017, Optical Society of America (OSA) Technical Digest JTu5A.1
J. Tam, J. Liu, A. Dubra, R. Fariss, “In vivo imaging of the human retinal pigment epithelial mosaic using adaptive optics enhanced indocyanine green ophthalmoscopy,” Investigative Ophthalmology and Visual Science 57(10):4376-4384, 2016
J. Liu, A. Dubra, J. Tam, “Computer-Aided Detection of Human Cone Photoreceptor Inner Segments Using Multi-scale Circular Voting,” SPIE Medical Imaging 2016: Computer-Aided Diagnosis, Vol 9785, 97851A
J. Liu, A. Dubra, J. Tam, “A Fully Automatic Framework for Cell Segmentation on Non-confocal Adaptive Optics Images,” SPIE Medical Imaging 2016: Computer-Aided Diagnosis, Vol 9785, 97852J