About our work

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.

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Custom software tools developed by the Tam lab are available through the NEI Commons, a virtual infrastructure to enable sharing. Visit the NEI Commons.

Selected publications

Peer-reviewed journal publications

N. Kedia, Z. Liu, R.D. Sochol, J. Tam, D.X. Hammer, A. Agrawal, “3-D Printed Photoreceptor Phantoms for Evaluating Lateral Resolution of Adaptive Optics Imaging Systems,” Optics Letters 44(7):1825-1828, 2019 (Editor’s pick)

H. Jung, J. Liu, T. Liu, A. George, M. Smelkinson, S. Cohen, R. Sharma, O. Schwartz, A. Maminishkis, K. Bharti, C. Cukras, L.A. Huryn, B.P. Brooks, R. Fariss, and J. Tam, “Longitudinal adaptive optics fluorescence microscopy reveals cellular mosaicism in patients,” JCI Insight 4(6):e124904, 2019

H. Jung*, T. Liu*, J. Liu, L. A. Huryn, and J. Tam, “Combining multimodal adaptive optics imaging and angiography improves visualization of human eyes with cellular level resolution,” Communications Biology 1:189, 2018 (*equal contribution) (Editor’s pick – first year anniversary collection)

J. Liu, H. Jung, A. Dubra, and J. Tam, “Cone Photoreceptor Cell Segmentation and Diameter Measurement on Adaptive Optics Images Using Circularly-Constrained Active Contour Model,” Investigative Ophthalmology and Visual Science 59(11):4639-4652, 2018

Z. Liu, J. Tam, O. Saeedi, D.X. Hammer, “Trans-retinal cellular imaging with multimodal adaptive optics,” Biomedical Optics Express 9(9):4246-4262, 2018

B. Gu, X. Wang, M.D. Twa, J. Tam, C.A. Girkin, and Y. Zhang, “Noninvasive in vivo characterization of erythrocyte motion in human retinal capillaries using high-speed adaptive optics near confocal imaging,” Biomedical Optics Express 9(8):3653-3677, 2018

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

W. Ma, Y. Zhang, C. Gao, R. Fariss, J. Tam, W. Wong, “Monocyte infiltration reestablishes retinal myeloid cell homeostasis following retinal pigment epithelial cell injury,” Scientific Reports 7:8433, 2017

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. Tam and D. Merino, “STORM in comparison with STED and other imaging methods,” Journal of Neurochemistry 135(4): 643-658, 2015

Peer-reviewed conference papers

J. Liu, C. Shen, T. Liu, N. Aguilera, and J. Tam, “Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images,” 6th MICCAI Workshop on Ophthalmic Medical Image Analysis (MW-OMIA6) 2019, Lecture Notes in Computer Science, Vol 11855, Springer, 2019

J. Liu, C. Shen, T. Liu, N. Aguilera, and J. Tam, “Active Appearance Model Induced Generative Adversarial Network for Controlled Data Augmentation,” Medical Imaging Computing and Computer-Assisted Intervention – MICCAI 2019, Lecture Notes in Computer Science, Vol 11764, Springer, 2019

J. Liu, H. Jung, J. Tam, “Computer-aided detection of pattern changes in longitudinal adaptive optics images of the retinal pigment epithelium,” 15th International Symposium on Biomedical Imaging (ISBI): 34-38, 2018

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, 2017

Conference proceedings

J. Liu, H. Jung, T. Liu, and J. Tam, “Longitudinal Matching of in vivo Adaptive Optics Images of Fluorescent Cells in the Human Eye Using Stochastically Consistent Superpixels,” SPIE Medical Imaging 2019: Computer Aided Diagnosis, Vol 10950, 1095030, 2019

J. Tam, “Adaptive Optics and its use in inflammatory eye disease,” Book Chapter in Multimodal Imaging in Uveitis, Springer International Publishing, 2018

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, 2017

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, 2016

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, 2016

Clinical and Translational Imaging Unit key staff

Key staff table
Name Title Email Phone
Nancy Aguilera, B.S. Adaptive Optics Imaging Technician nancy.aguilera@nih.gov
Andrew Bower, Ph.D. Postdoctoral Fellow andrew.bower@nih.gov
John Giannini, Ph.D. Postdoctoral Fellow john.giannini@nih.gov
Joanne Li, Ph.D. Postdoctoral Fellow joanne.li@nih.gov
Tao Liu, Ph.D. Adaptive Optics Research Scientist tao.liu@nih.gov
Jianfei Liu, Ph.D. Adaptive Optics Engineer jianfei.liu@nih.gov
Rongwen Lu, Ph.D. Postdoctoral Fellow rongwen.lu@nih.gov
Johnny Tam, Ph.D. Stadtman Investigator johnny@nih.gov 301-435-7821

Clinical and Translational Imaging Unit alumni

Name Title Time Period
HaeWon Jung Advanced Retinal Imaging Specialist -
Christine Shen Student IRTA -
Yoo-Jean Han Summer Intern -

News from this lab

Field of spots with colors ranging from yellow to dark purple.

Imaging Method Reveals Long-lived Patterns in Cells of the Eye

March 21, 2019

Cells of the retinal pigment epithelium (RPE) form unique patterns that can be used to track changes in this important layer of tissue in the back of the eye, researchers at the National Eye Institute (NEI) have found.
Images show multimodal technique using adaptive optics and angiography to simultaneously see photoreceptors (left), retinal pigment epithelial cells (center), and choriocapillaris in the living human eye.

NIH scientists combine technologies to view the retina in unprecedented detail

November 14, 2018

By combining two imaging modalities—adaptive optics and angiography—investigators at the National Eye Institute (NEI) can see live neurons, epithelial cells, and blood vessels deep in the eye’s light-sensing retina.
Last updated: May 12, 2020