Optical Physics, Quantum Dots, Contact Lens, 3D Optical Imaging and Visualization, Artificial Neural Network
Field of Research (FoR)
Maitreyee Roy is a Senior Lecturer in the School of Optometry and Vision Science at the University of New South Wales, Australia. She was awarded a Ph.D. in Physical Optics from School of Physics at the University of Sydney. Her PhD research was focused on the fundamental principle of “Geometric Phase” in optics and its application for 3D imaging for a biological and non-biological sample. One of her major contributions in this area was to...view more
Maitreyee Roy is a Senior Lecturer in the School of Optometry and Vision Science at the University of New South Wales, Australia. She was awarded a Ph.D. in Physical Optics from School of Physics at the University of Sydney. Her PhD research was focused on the fundamental principle of “Geometric Phase” in optics and its application for 3D imaging for a biological and non-biological sample. One of her major contributions in this area was to demonstrate achromatic nature of geometric phase, which has opened up new insights into broadband interferometry with applications ranging from biological systems (Optical Coherence Tomography), electronics (Semiconductor industry) to astronomy (Stellar Interferometry).
Prior to joining UNSW in 2015, she worked in multidisciplinary research projects in government and academic institutions including the School of Physics and Australian Key Centre for Microscopy & Microanalysis at the University of Sydney and National Measurement Institute, Australia (NMIA). While at NMIA as a Research Scientist, she conducted and led various Optical Standards and Nano-metrology projects focusing on establishing and maintaining the capabilities underpinning the delivery of measurement services for industry, government and to the research community.
She has also worked in several international research labs: Charles Fabry Laboratory, University d’ Orsay, France; Department of Applied Physics, Osaka University; National Measurement & Standards Laboratory, NRC, Canada; Department of Optics, National Institute of Astrophysics, Optics and Electronica, Mexico and Mechanical Engineering Laboratory, Tsukuba, Japan.
- Automated detection of ocular diseases with Artificial Intelligence.
- Development of novel instrumentation and imaging technology for rapid, sensitive, non-contact, non-invasive methods for high-resolution three-dimensional imaging for biomedical applications which incorporate the Full-Field optical coherence microscopy based on liquid crystal geometric phase-shifting technology coupled with fluorescence microscopy.
- Exploring the use of functionalized nanoparticle as a contrast agent in bio-imaging and Its Physico-chemical characterizations using Field-Flow Fractionation coupled with a series of light scattering detectors and Inductively Coupled Plasma Mass Spectrometry.
- The effect of blue-blocking lenses on visual and non-visual functions.
TEACHING & OUTREACH
Courses I teach and coordinate
- VISN1111 - Geometrical and Physical Optics
- VISN1221 - Visual Optics
- Member of the Australian Microscopy and Microanalysis Society Inc
- Member of the Australian Optical Society
- Member of the Metrological Society Australia
- Member of the Optical Society of America
- Member of the Optical Society of India
- Member of the International Society for Optical Engineering
AWARDS & ACHIEVEMENTS
- NMI World Metrology Day Outstanding Achievement Awards, 2013- NETs Team: For an outstandingly successful program delivering world-class measurement standards and services and for active and effective engagement with a wide range of stakeholders to focus and apply these capabilities
- NMI World Metrology Day Outstanding Achievement Awards, 2011- Optical Standards Team: Excellent work in satisfying optical measurement requirements of a major strategic client, Department of Defence
- ProSciTech Trans-Tasman Award,2006, For significant research using microscopy or microanalysis, especially work for lasting value to people/ or the environment, Australian Microscopy and Microanalysis Society
- Australian Academy of Science: France-Australia Science Innovation Collaboration Program, 2007
- Denison travel fellowship, School of Physics, University of Sydney,2006
- JSPS invitation fellowship (Japan Society for Promotion of Science), 2004
- Australian Academy of Science travel fellowship, 2003, “Scientific visits to the United States of America, Canada and Mexico” scheme
- AIST Fellow (Agency of Industrial Science and Technology), Japan, 1999
- Daimler-Benz Award, 1999- Best overall papers by the collaborative team (Japanese) presented at 31st International Symposium on Automotive Technology & Automation, Germany
- Automated detection of ocular diseases with Artificial Intelligence.
- Blue light blocking lenses, effects on visual and non-visual systems
- Functionalised nanoparticles as contrast agents for bio-imaging
- Optical coherence microscopy for ultrahigh-resolution 3D imaging
- UNSW SEED Grant, 2019
My Research Activities
Investigation of Blue light blocking lenses effect on visual and non-visual systems
Purpose: “Blue-blocking” lenses (BBLs) are being marketed to protect retinae against hazardous blue light and to restrict the wavelengths that may be related to melatonin suppression and sleep quality. Any lenses that preferentially transmit some wavelengths more than others have the potential to affect visual performances. The aim of the present study is two folds: (a) To evaluate the optical and perceptual effect of blue-blocking lenses on colour contrast sensitivity to low and high contrast targets. (b) To quantify the colour discrimination threshold for two types of blue-blocking lenses with and without powers under mesopic and photopic conditions.
Automated detection of ocular diseases with Artificial Intelligence
Currently, artificial intelligence (AI), especially Machine learning (ML), techniques are revolutionising healthcare for its potential in image-based diagnosis, disease-prognostication, and risk-assessment. In ophthalmology, AI is also becoming common for screening, image-interpretation, early diagnosis and guiding treatment of eye conditions. This research will devise and evaluate new clinically meaningful metrics for analysing ocular images, implement novel machine and deep-learning algorithms for automatic segmentation, disease detection and progression-classification of eye diseases using both fundus photographs and optical coherence tomography (OCT) images from both healthy subjects and patients undergoing treatment for eye disease.
Visualising Tear Film Lipid Layer Using Quantum Dots
The most recent and rapidly growing field of nanotechnology has given an immense innovatory idea to the science. Among those, optometry also gained high interest in nanotechnology in recent decades. Regarding imaging of various parts of the eyes, QDs have been used for uveal melanoma, tracking of uveal flow for the treatment of glaucoma and better visualization. All these researches were focused on the posterior of the eyes for various purposes. This project is focused on one of the anterior layers of the eye that is tear film specifically lipid layer. Using the optical properties of the QDs, it is aimed to have a better visualisation of this layer whose composition is known but the structural arrangement and interfacial interactions are still under research. Various molecular and dynamic models have been proposed to illustrate the dynamic structural arrangement of lipids in this layer. These simulations need to be experimentally verified to develop authenticated structural models of lipids and their replenishment.
3D reconstruction and visualization of ocular disease model from OCT images with virtual reality
3D reconstruction of medical images helps in image interpretation with visualising depth and understanding the underlying pathological process in disease. Additionally, the modern technology of 3D visualisation with virtual or augmented reality makes the treatment and diagnosis process very faster and more comfortable even in a surgical setting for all clinicians. It also helps the patient to understand the state of his/her disease very evidently. In this project, the 2D slices of OCT images of glaucoma patients will be collected and reconstructed with volume rendering process in different 3D software and afterwards, the 3D model will visualize with the virtual reality devices. Moreover, the project will also comprise some 3D animation and roaming in the visualisation environment to make it more realistic for the understanding of disease for diagnosis purpose. One of the goals of this project is to measure its impact on patient health literacy, to determine if this method can be used to enhance patient understanding of ocular disease processes.
Machine learning approaches in ocular images analysis: Automated detection and diagnosis
With the increasing prevalence of ocular diseases like retinal detachment, diabetic retinopathy (DR) and age-related macular degeneration (AMD), annual screening for ocular diseases by human expert grading of retinal images are challenging. Automated retinal image assessment systems (ARIAS) may provide clinically effective and cost-effective detection of retinopathy. We aim to determine whether ARIAS can be safely introduced by machine learning into appropriate retinal screening pathways to replace human experts. Recently, machine learning approaches have become increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This research project will highlight new research directions and examine the main challenges related to machine learning in ocular imaging, applying novel deep learning algorithms to automatic analysis of both digital fundus photographs and OCT images from both healthy control subjects and patients undergoing treatment for relevant ocular diseases.
My Research Supervision
Areas of supervision
- Development of novel instrumentation and imaging technology for rapid, sensitive, non-contact, non-invasive methods for high-resolution three-dimensional imaging for biomedical applications which incorporate the Full-Field optical coherence microscopy based on liquid crystal geometric phase-shifting technology coupled with fluorescence microscopy
- Investigation of Blue light blocking lenses effect on visual and non-visual systems.
- Automated detection of ocular diseases with Artificial Intelligence
- Visualising Tear Film Lipid Layer Using Quantum Dots
Three postgraduate and 12 undergraduate students
- The Computational Imaging Lab/Berkeley Artificial Intelligence Research (CIL/BAIR) Lab, A/Prof Laura Waller, USA, Lead investigator, Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
- Deputy Director, Optics and Radiometry Laboratory (ORLAB), SOVS, UNSW Sydney
- Prof. Kiyofumi Matsuda, Emeritus Professor at Tokyo Institute of Technology, Optical Information Processing and Systems Engineering Lab, The Graduate School for the Creation of New Photonics Industries, Japan
- Centre for Eye Health (CFEH), UNSW Sydney