Rate My Professor Andrew Mehnert

Mehnert’s primary research specializations lie in the development of advanced image analysis methods for biomedical applications, particularly involving MRI, CT, and optical microscopy. His work includes techniques for image visualisation, denoising, spatial co-registration, segmentation, parametric modelling of dynamic contrast-enhanced MRI (DCE-MRI), computational diffusion MRI, and machine learning-based classification. His contributions have significantly impacted fields such as ophthalmology, with recent key publications including “A computational learning pipeline for glaucoma progression detection based on the prediction of visual field changes from fundus photographs” (2026, Expert Systems with Applications), “Interpericyte tunnelling nanotube loss in very early diabetic retinal disease” (2026, Diabetologia), “A comprehensive survey of imaging-based methods of measuring intracranial pressure” (2025, Biomedical Signal Processing and Control), “Multiscale guided attention network for optic disc segmentation of retinal images” (2025, Computer Methods and Programs in Biomedicine Update), and “Predictors of Peripheral Retinal Non-Perfusion in Clinically Significant Diabetic Macular Edema” (2025, Journal of Clinical Medicine). Mehnert has authored or co-authored 74 research outputs, including 38 journal articles and 23 conference papers. He has secured competitive funding, such as an EU Marie Curie International Reintegration Grant (2011-2014) for breast cancer imaging and Australian National Data Service grants (2017) for image data management. As group leader, he supports multidisciplinary research at UWA through expertise in data analysis and visualisation, contributing to advancements in health and well-being.