Research

Research Interests

My research interests focus on two scientific areas:

  1. Understanding how the retina learns to processing visual stimuli.
  2. The interface of hardware, software and wetware in the pursuit of high quality, quantitative bioimaging.

Both of these areas requires the integration of skills from computing, engineering, chemistry and the life sciences. As such, my research career has moved me between disciplines and involves a large number of cross-disciplinary collaborations.

You can find my publications here.

Current Research

How can babies see from the moment their born? How does the retina learn to process images before sending them to the brain? My current research is aimed at understanding these fundamental neuroscience questions by combining state-of-the-art light sheet microscopy, image analysis and network science.

I am doing this in the zebrafish (Danio Rerio), a popular model organism in microscopy due to it’s optical transparency and the wide range of fluorescence transgenic lines now available. Zebrafish are even more attractive as models for retinal developmental biology due to their relative simplicity but core similarities to mammalian retinal development.

Projects

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Working with the Imaging Concepts Group (University of Glasgow) I am interested in comparing how different computational imaging approaches to calcium imaging effect the analysis of calcium signalling between neurons.

As an EPSRC Doctoral Prize Research Fellow (University of Glasgow) I have begun to explore the challenges of imaging eye development in zebrafish. The two key challenges are the optical power of the eye lens, which focusses light in a way undesirable for imaging, and the light-sensitive nature of the retina.

As Research Assistant to Dr Jonathan Taylor (Glasgow University) I worked on real-time image processing for the synchronisation of 3D images of the in vivo, beating zebrafish heart. Through this project we have developed a technique we call adaptive prospective optical gating. The constant motion of the heart is a major obstacle to live imaging and, in the past, fish have been fixed or their heartbeats have been pharmaceutically slowed down.

Working with Cigdem Sazak and Boguslaw Obara (Durham University) I developed a new approach for the enhancement of vessel-like objects in biological and medical imaging called the bowler-hat transform. We have used this approach in the analysis of fungal and slime mould networks, with our collaborator Mark Fricker (Oxford University), retinal imaging and brain vasculature. My contribution to this work was funded by the EPSRC. The key output of this work was published in Pattern Recognition.

As an EPSRC Doctoral Prize Research Fellow (University of Glasgow), I am investigated the use of graph theory, the mathematical background to network science, to understand how networks of neurons change organisation throughout development. In particular, I am developing analysis pipelines for the quantitative exploration of retinal development as measured by multi-electrode array (MEA) recordings and calcium imaging.

At Durham University, my PhD research focussed on the use of mathematical morphology to quantify biological and medical images. One such challenge was the automated detection of ellipse-like objects, such as cell nulcei, in fluorescence microscopy. Working with Philip Jackson and Boguslaw Obara, I developed a new approach to identifying ellipses based on Hilbert-edge detection and ranging (HEDAR) and assumptions based on the shape on an ellipse. My contribution to this work was funded by EPSRC.

Working with researchers at Durham University, the University of Essex and the University of Exeter, I published an article focussing on the benefits of using object-based approaches to quantify colocalisation experiments. The article highlighted not only the benefits of such an approach but the importance of understanding the underlying algorithms and their assumptions by showing the biases and uncertainties of the approach. My contribution to this work was funded by EPSRC.