Zhihong (Jewel) Hu, PhD, ECE

Dr. Hu leads the Doheny Eye Image Analysis Laboratory (DIAL) for the advanced software development in ophthalmic image processing and analysis.

Ophthalmic images encode rich information about eye structures and eye diseases. One of Dr. Hu’s research interests is to utilize the cutting-edge technology in artificial intelligence (AI), machine learning, deep learning, and computer vision to advance the fields of automated screening and classification, novel biomarkers’ discovery, quantitative prediction of progressive growth, and image segmentation and registration on 2D and 3D images with various eye diseases, e.g. age-related macular degeneration (AMD), juvenile macular degeneration, glaucoma, and laser-induced retinal injury.

Dr. Hu’s another interest is the development of advanced web-based graphical user interfaces (GUI) and grading software tools with image data management, conversion, visualization, annotation, and analysis to support clinical trials and clinical research.  

Fasih-Ahmad S, Wang Z, Mishra Z, Vatanatham C, Clark ME, Swain TA, Curcio CA, Owsley C, Sadda SR, Hu ZJ. Potential Structural Biomarkers in 3D Images Validated by the First Functional Biomarker for Early Age-Related Macular Degeneration – ALSTAR2 Baseline. Invest Ophthalmol Vis Sci. 2024 Feb 1;65(2):1. doi: 10.1167/iovs.65.2.1. PMID: 38300559; PMCID: PMC10846345.

Wang S, Wang Z, Vejalla S, Ganegoda A, Nittala MG, Sadda SR, Hu ZJ. Reverse engineering for reconstructing baseline features of dry age-related macular degeneration in optical coherence tomography. Sci Rep. 2022 Dec 31;12(1):22620. doi: 10.1038/s41598-022-27140-8. PMID: 36587062; PMCID: PMC9805430.

Wang, S. R. Sadda, A. Lee, Z. Hu. Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks. Sci Rep. 2022 Aug 26;12(1):14565. doi: 10.1038/s41598-022-18785-6. PMID: 36028647; PMCID: PMC9418226.

S Saha, Z Wang, S Sadda, Y Kanagasingam, Z. Hu, Visualizing and understanding inherent features in SD‐OCT for the progression of age‐related macular degeneration using deconvolutional neural networks, Applied AI Letters 1 (1), first published: 14 October 2020

Mishra, A. Ganegoda, J. Selicha, Z. Wang, S. R. Sadda, Z. Hu, “Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information”, Scientific Reports volume 10, Article number: 9541 (2020)

S. Saha, M. Nassisi, M. Wang, S. Lindenberg, Y. Kanagasingam, S. Sadda, Z. Hu, “Automated detection and classification of early AMD biomarkers using deep learning”, Scientific Reports, (2019) 9:10990

Wang, S. Sadda, Z. Hu, “Deep learning for automated screening and semantic segmentation of age-related and juvenile atrophic macular degeneration“, Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109501Q (2019)

Hu, X. Wu, Y. Ouyang, Y. Ouyang, Srinivas R. Sadda, “Semiautomated Segmentation of the Choroid in Spectral-Domain Optical Coherence Tomography Volume Scans”, Invest. Ophthalmol. Vis. Sci., vol. 54, no. 3, pp. 1722-1729, 2013. PMID: 23349432.

Hu, G. G. Medioni, M. Hernandez, A. Hariri, X. Wu, S. R. Sadda, “Segmentation of the Geographic Atrophy in Spectral-Domain Optical Coherence Tomography and Fundus Autofluorescene Images“, Invest. Ophthalmol. Vis. Sci., December 30, 2013, vol. 54, no. 13, 8375-8383. PMID: 24265015.

Hu, M. Niemeijer, M. D. Abràmoff, M. K. Garvin, “Multimodal Retinal Vessel Segmentation from Spectral-Domain Optical Coherence Tomography and Fundus Photography“, IEEE Transactions on Medical Imaging, 31(10): 1900-1911 (2012). PMID: 22759443. PMCID: PMC4049064.

Hu, M. D. Abràmoff, Y. H. Kwon, K. Lee, and M. K. Garvin, “Automated Segmentation of Neural Canal Opening and Optic Cup in 3D Spectral Optical Coherence Tomography Volumes of the Optic Nerve Head,” Invest. Ophthalmol. Vis. Sci., vol. 51, no. 11, pp. 5708–5717, 2010. PMCID: PMC3061507.

Grant award: Discovery and validation of AMD biomarkers for progression using deep learning, NIH NEI R21, Lead PI
Grant award: Artificial intelligence for assessment of Stargardt macular atrophy, NIH NEI R21, Lead PI
Grant award: Automated multimodal detection and analysis of geographic atrophy, BrightFocus, PI
Grant award: Automated detection of retinal injury (Phase I), DHA, Subaward investigator
Grant award: High resolution OCTA axial profiles in health and disease, Heidelberg Engineering, PI
Grant award: Functionally validated structural endpoints for early AMD, NIH NEI R01, Co-investigator
Grant award: Multimodal image analysis in age-related macular degeneration, MVRF, Project lead
License: GUI software grading tools (OCTOR & GRADOR), ZOC, Sun Yat-sen University, Project lead
NEI travel grant award, ARVO
ARVO abstract selected as hot topic
Nomination for MIT outstanding poster award, ARVO
Graduate student senate travel funds award, UI
Excellence in electrical and computer engineering graduate fellowship award, UI
IEEE TMI distinguished reviewer
Expert panelist, Strategic plan: vision for the future data science panel, NIH NEI