Clinical and Translational Imaging Section

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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Resources

Custom software tools developed by the Tam lab are available through the NEI Commons, a virtual infrastructure to enable sharing. a href="https://neicommons.nei.nih.gov/">Visit the NEI Commons.

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Selected publications

Peer-reviewed journal publications

V. Das, F. Zhang, A.J. Bower, J. Li, T. Liu, N. Aguilera, B. Alvisio, Z. Liu, D.X. Hammer, and J. Tam, “Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography ,” Communications Medicine 4:68, 2024

J. Li*, D. Wang*, J. Pottenburgh, A.J. Bower, S. Asanad, E.W. Lai, C. Simon, L. Im, L.A. Huryn, Y. Tao, J. Tam, and O.J. Saeedi, “Visualization of erythrocyte stasis in the living human eye reveals microvascular dysfunction in disease,” (*equal contribution), iScience 26(1):105755, 2023

H. Heitkotter*, E.J. Patterson*, E.N. Woertz, J.A. Cava, M. Gaffney, I. Adhan, J. Tam, R.F. Cooper, and J. Carroll, “Extracting spacing-derived estimates of rod density in healthy retinae,” (*equal contribution) Biomedical Optics Express 14 (1):1-17, 2023

N. Aguilera, T. Liu, A.J. Bower, J. Li, S. Abouassali, R. Lu, J. Giannini, M. Pfau, C. Bender, M.G. Smelkinson, A. Naik, B. Guan, O. Schwartz, A. Volkov, A. Dubra, Z. Liu, D.X. Hammer, D. Maric, R. Fariss, R.B. Hufnagel, B.G. Jeffrey, B.P. Brooks, W.M. Zein, L.A. Huryn, and J. Tam, “Widespread subclinical cellular changes revealed across a neural-epithelial-vascular complex in choroideremia using adaptive optics,” Communications Biology 5:893, 2022

T. Liu, N. Aguilera, A.J. Bower, J. Li, E. Ullah, A. Dubra, C. Cukras, B.P. Brooks, B.G. Jeffrey, R.B. Hufnagel, L.A. Huryn, W.M. Zein, and J. Tam, "Photoreceptor and retinal pigment epithelium relationships in eyes with vitelliform macular dystrophy revealed by multimodal adaptive optics imaging," Investigative Ophthalmology and Visual Science 63(8):27, 2022

J.P. Giannini, R. Lu, A.J. Bower, R. Fariss, and J. Tam, “Visualizing retinal cells with adaptive optics imaging using a translational imaging framework,” Biomedical Optics Express 13(5):3042-3055, 2022

J. Liu, C. Shen, N. Aguilera, C. Cukras, R.B. Hufnagel, W.M. Zein, T. Liu, and J. Tam, “Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images,” IEEE Transactions on Medical Imaging 40(1):2820-2831, 2021

R. Lu, N. Aguilera, T. Liu, J. Liu, J.P. Giannini, J. Li, A.J. Bower, A. Dubra, and J. Tam, “In vivo sub-diffraction adaptive optics imaging of photoreceptors in the human eye with annular pupil illumination and sub-Airy detection,” Optica 8(3):333-343, 2021

J. Li*, T. Liu*, O.J. Flynn, A. Turriff, Z. Liu, E. Ullah, J. Liu, A. Dubra, M.A. Johnson, B.P. Brooks, R.B. Hufnagel, D.X. Hammer, L.A. Huryn, B.G. Jeffrey, J. Tam, “Persistent dark cones in oligocone trichromacy revealed by multimodal adaptive optics ophthalmoscopy” (*equal contribution), Frontiers in Aging Neuroscience 13:629214, 2021

A.J. Bower, T. Liu, N. Aguilera, J. Li, J. Liu, R. Lu, J.P. Giannini, L.A. Huryn, A. Dubra, Z. Liu, D.X. Hammer, and J. Tam, “Integrating adaptive optics-SLO and OCT for multimodal visualization of the human retinal pigment epithelial mosaic,” Biomedical Optics Express 12(3):1449-1466, 2021

J. Liu, Y. Han, T. Liu, N. Aguilera, and J. Tam, “Spatially Aware Dense-LinkNet Based Regression Improves Fluorescent Cell Detection in Adaptive Optics Ophthalmic Images,” IEEE Journal of Biomedical Health Informatics 24(12):3520-3528, 2020 (Journal Cover; Featured Article)

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 (Journal Cover)

Peer-reviewed conference papers

J. Liu, N. Aguilera, T. Liu, and J. Tam, “Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images,” MICCAI workshop on Data Augmentation, Labeling, and Imperfections (DALI), Lecture Notes in Computer Science, Vol 13003, Springer, 2021

J. Liu, J. Li, T. Liu, and J. Tam, “Graded Image Generation Using a Stratified CycleGAN,” Medical Imaging Computing and Computer-Assisted Intervention – MICCAI 2020, Lecture Notes in Computer Science, Vol 12262, Springer, 2020

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,” 15^th 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

Other Publications

V. Das, F. Zhang, A.J. Bower, B. Alvisio, Z. Liu, D.X. Hammer, and J. Tam, “Deep learning enables visualization of individual RPE cells from a single AO-OCT volume,” SPIE Photonics West, Vol 12360, 1236004

J. Li, N. Aguilera, T. Liu, A.J. Bower, J.P. Giannini, C. Cukras, T. Keenan, E. Chew, B.P. Brooks, W.M. Zein, L.A. Huryn, R.B. Hufnagel, and J. Tam, “Structural integrity of retinal pigment epithelial cells in eyes with age-related scattered hypofluorescent spots on late phase indocyanine green angiography (ASHS-LIA),” Eye 2022

J. Liu, J. Li, A. Wolde, C. Cukras, and J. Tam, “Hybrid Transformer for Lesion Segmentation on Adaptive Optics Retinal Images,” SPIE Medical Imaging 2022: Computer Aided Diagnosis, Vol 12033, 120331W

J. Liu, Y. Han, T. Liu, and J. Tam, “Spatially Aware Deep Learning Improves Identification of Retinal Pigment Epithelial Cells with Heterogeneous Fluorescence Levels Visualized Using Adaptive Optics,” SPIE Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, Vol 11317, 1131719

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