Current HDR Students

Shashank Bhandary | Azam Darvishi | Samaneh Hashemi | Asif Iqbal | Mahdi Heravian Shandiz | Jason Kugelman

 

Shashank Bhandary

Thesis Title: Using objective methods to understand the effects of near activities on myopia-genic visual environment factors

Shashank arrived in Brisbane, in July 2023, ready to commence full-time higher degree research studies within QUT’s Optometry and Vision research discipline. Shashank’s research project will utilise novel objective measurement methods to investigate visual environment factors associated with myopia. This will provide new insights into the impact of the modern visual environment and visual tasks upon myopia, and potentially identify novel treatment methods and strategies for preventing myopia development. Shashank’s thesis is guided by principal supervisor, Professor Scott Read, and Associate Supervisors: Prof. Stephen Vincent and Prof. Michael Collins.

 

 

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Azam Darvishi

Thesis Title: Investigation of the effects of different wavelengths of light on myopia

Azam commenced her PhD candidature in June 2023. Azam’s Principal Supervisor is Professor Scott Read, with supervisory support being provided by Professor Stephen Vincent.

Azam’s research project will investigate the effects of different wavelengths of light, on myopia. Despite the relationship between light intensity and myopia, the effects of various light wavelengths and their effects upon myopia have gained momentum and interest. Refractive growth is influenced by the ambient spectral composition. The purpose of Azam’s research study is to determine the effects of short-wavelength lighting and long-wavelength lighting on choroidal thickness in humans.

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Samaneh Hashemi

Thesis Title: Ocular/Tissue blood flow imaging using optical coherence tomography angiography

Samaneh commenced her PhD journey as a full-time QUT student in May 2023. Her research project will focus on the optical coherence tomography angiography (OCT-A) images of the human eye with an emphasis on quantifying image processing and overcoming specific OCT-A’s imaging limitations in the posterior area of the eye. Samaneh’s QUT HDR Principal Supervisor is Professor Zhiyong Li (School of Mech. Med. & Proc. Eng.), with supervisory support provided by Centre for Vision & Eye Research staff:  Dr David Alonso Caneiro, Professor Scott Read and Professor Michael Collins.

 

 

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Asif Iqbal

Thesis Title: Tear exchange during scleral lens wear and its association with central and peripheral corneal oedema with different lens fitting characteristics

Asif Iqbal commenced his PhD candidature in the Contact Lens and Visual Optics Laboratory at QUT’s School of Optometry and Vision Science, in October 2022. Asif’s Principal Supervisor is Professor Stephen Vincent, and his Associate Supervisors are Professor Michael Collins, Dr Damien Fisher and Dr David Alonso-Caneiro.

Asif’s research project aims to quantify tear exchange during scleral lens wear, with a focus on the occurrence of central and peripheral corneal oedema when scleral lenses with differing lens fitting characteristics are introduced. Asif also plans to investigate how varying lens modification options may improve tear exchange and ensue a positive impact on corneal physiology.

Publications (arising from thesis):

Iqbal, AsifFisher, DamienAlonso Caneiro, DavidCollins, Michael, & Vincent, Steve (2023) Central and peripheral scleral lens-induced corneal oedema. Ophthalmic and Physiological Optics.

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Mahdi Heravian Shandiz

Thesis Title: Towards a more efficient platform for analysing retinal images via machine learning

 

Mahdi commenced his PhD candidature as a part-time external student in May 2021. Mahdi’s Principal Supervisor is Dr David Alonso-Caneiro, with Professor Michael Collins and Professor Scott Read providing supervisory mentorship in their roles of Associate Supervisors. Mahdi’s research project will investigate the use of machine learning methods for the analysis and quantification of medical images of the human eye, with a particular focus on the posterior segment of the eye.

 

 

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Jason Kugelman

Thesis Title: Application of generative deep learning models in ophthalmic imaging

 

Jason started his PhD studies at QUT in January 2021. His research project will investigate generative deep learning methods such as generative adversarial networks (GANs), and their application to ophthalmic images as well as the potential extensibility of new and modified techniques beyond this domain. Applications include data augmentation, super resolution, de-noising + de-artifacting, domain translation, image segmentation and anomaly detection, among others. The aim is to use these methods to improve image analysis in ophthalmology, supporting research, clinical practice and diagnostics, while the potential of applying new methods beyond this domain is likely to have general positive benefits in the area of deep learning-based image analysis.

Publications (arising from thesis):

Kugelman, JasonAlonso-Caneiro, DavidRead, Scott A.Vincent, Stephen J., & Collins, Michael J. (2023) Enhanced OCT chorio-retinal segmentation in low-data settings with semi-supervised GAN augmentation using cross-localisation. Computer Vision and Image Understanding237, Article number: 103852.

Kugelman, JasonAlonso-Caneiro, DavidRead, Scott A., & Collins, Michael J. (2022) A review of generative adversarial network applications in optical coherence tomography image analysis. Journal of Optometry15, S1-S11.

Kugelman, JasonAllman, JosephRead, Scott A.Vincent, Stephen J.Tong, JanelleKalloniatis, Michael, et al. (2022) A comparison of deep learning U-Net architectures for posterior segment OCT retinal layer segmentation. Scientific Reports12(1), Article number: 14888.

Kugelman, JasonAlonso-Caneiro, DavidRead, Scott A.Vincent, Stephen J., & Collins, Michael J. (2021) OCT chorio-retinal segmentation with adversarial loss. In Zhou, JunSalvado, OlivierSohel, FerdousBorges, Paulo Vinicius K., & Wang, Shilin (Eds.) Proceedings of the 2021 Digital Image Computing: Techniques and Applications (DICTA). Institute of Electrical and Electronics Engineers Inc., United States of America.

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