Nigerian wins award in ophthalmology research

A Nigerian doctoral student at the Brunel University, London, Bashir Isah Dodo, has been given an award for the ‘Best Student Paper’ at the recently concluded BIOIMAGING 2018 Conference in Portugal.

Bashir’s developed a new technique that quickens the identification and diagnosis of damage to the human retina.

29-year-old Dodo, from Katsina State, is a doctoral student at the Department of Computer Science at the Brunel University, London.

He also holds a Bachelor’s degree in Software Engineering and a Master’s degree in Computer Systems Engineering (Software Systems) at the University of East London.

Bashir’s research work on ‘Intra-retinal Layer Segmentation in Spectral Domain Optical Coherence Tomography Images’ discovered a new algorithm for Optical Coherence Tomography (OCT) equipment.

Brunel University says that the technique is capable of automatically segmenting images of the retina into seven distinct layers, thus improving the accuracy and speed of diagnosis, and helping to save the sight of patients by identifying damage earlier.

Based on the psychological concept of similarity, Bashir used the ideas of continuity and discontinuity to develop an OCT algorithm that can identify where one layer of the retina transitions to the next.

OCT machines are used by ophthalmologists to produce non-invasive three-dimensional photographs of a patient’s retina, which are used in the diagnosis of eye-related disease.

According to Bashir, “Layer segmentation is one of the early processes of OCT retina image analysis, and already plays an important role in clinics.

“For example, the thickness profile of the Retinal Nerve Fibre Layer – which can be calculated directly from the segment layer – is used in the diagnosis of glaucoma, which is one of the most common causes of sight loss worldwide.

“Automatically segmenting the layers could provide critical information for abnormality detection by comparing them to the average population, and monitoring the progress of disease against previous scans.”

Whilst doctors are currently able to identify the layers manually from OCT images, Bashir’s new technique automatically segments images of the retina, allowing specialists to spot abnormalities quicker, and better track the progress of medication.

He says, “It is evident that prior knowledge plays an important role in diagnosis; therefore using automated methods to look back through medical records or ophthalmology literature has great potential to influence how this field progresses.”