Cian Scannell
Department / Institute
Group
RESEARCH PROFILE
Cian Scannell is an assistant professor in the Department of Biomedical Engineering, where he works in the Medical Image Analysis (IMAG/e) group. His research topic is automated medical image analysis, primarily using deep learning. He has a particular interest in the analysis of cardiac MRI data and extensive experience in quantitative myocardial perfusion. Looking forward, he aims to bridge the gap between the development of automated image analysis tools and their deployment in the clinic. To achieve this goal, he will focus on developing models that are more generalisable and robust to data from different domains, such as different hospitals and scanners. He will also aim to improve reliability by incorporating domain knowledge such as the relevant physics and anatomy/physiology, and developing quality assurance steps.
Radiologists are often overworked and under pressure, and thus they could look at the same scans twice and come up with different interpretations. We aim to improve this using artificial intelligence for automatic and quantitative image analysis.
ACADEMIC BACKGROUND
Cian Scannell studied Mathematical Sciences at University College Cork (Ireland) where he graduated with first-class honours in 2016. He then moved to King’s College London to undertake his master's and PhD degrees as part of the Centre for Doctoral Training in Medical Imaging. Here, he worked in close collaboration with St Thomas’ Hospital and the industrial partners, Philips Healthcare. In 2021, he received his PhD and then continued for a further year as a postdoctoral researcher funded by the Wellcome/EPRSC Centre for Medical Engineering at King’s College London with a project on the development of robust and generalisable models for clinical integration. In 2022, he was appointed assistant professor with the Medical Image Analysis group in the Department of Biomedical Engineering.
Recent Publications
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Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records
Informatics in Medicine Unlocked (2024) -
High-resolution quantification of stress perfusion defects by cardiac magnetic resonance
European Heart Journal. Imaging Methods and Practice (2024) -
AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance
European Heart Journal - Digital Health (2023) -
Deep learning applications in coronary anatomy imaging
Journal of Medical Artificial Intelligence (2022) -
Optimized automated cardiac MR scar quantification with GAN-based data augmentation
Computer Methods and Programs in Biomedicine (2022)
Current Educational Activities
Ancillary Activities
No ancillary activities