Noble Group

Welcome to the Noble Group website

The Noble Group is led by Professor Alison Noble OBE FRS FREng. Our research interest is in biomedical image analysis, spanning cardiac magnetic resonance image analysis, to fetal ultrasound image analysis, to cell image analysis.

A particular focus for the group has been understanding the interplay of ultrasound device design (physics), clinical acquisition, and downstream image analysis and computer vision. Much of our recent work has concerned machine learning in ultrasound. We are interested in answering questions such as why is ultrasound scanning and interpretation hard to learn? How can we get computers to mimic what a skilled sonographer can do? What is the role of multi-modal analysis (such as eye-tracking and ultrasound video) in ultrasound imaging? And, can ultrasound be simplified (assisted by AI-assistive technologies) so that minimally trained clinical professionals can use it in non-traditional healthcare settings. Our research is motivated by unmet important clinical needs in developed world and developing world settings, and involves inter-disciplinary translational research collaborations with clinical groups from the UK and overseas.

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Projects that we lead

PULSE - We apply the latest ideas from machine learning and computer vision to perform sonography data science and build computational models of ultrasound image analysis and guidance tasks from large scale multi-modal ultrasound, eye tracking, probe movement and audio data.

PURFECT - We aim to build a gaze-assisted ultrasound guidance system to guide a non-expert to perform pregnancy growth scans.

TraCer (PRECISE) - We develop an easy-to-use point-of-care low-cost ultrasound-based device for fetal gestational age estimation at any point in pregnancy for LMIC settings.

CALOPUS - We combine easy-to-learn ultrasound sweep protocols with novel machine-learning algorithms for pregnancy risk assessment in India healthcare settings.

Projects that we partner on

SEEBIBYTE - We develop novel algorithms for spatio-temporal multi-task prediction, and self-supervised video representation learning for fetal ultrasound image analysis inspired by computer vision advances.

SMARTHEART - We develop spatio-temporal convolutional networks for cardiac motion estimation and regional analysis of LV function to characterize differences between healthy and diseased hearts.

CIFASD 4 - We develop biomedical image analysis algorithms for automatic facial image landmark detection, and ultrasound image analysis to characterize healthy and alcohol exposed fetal brains.

BRC IMAGING THEME - We develop new biomedical image analysis algorithms and perform sonography data science for first, second and third trimester fetal sonography in a hospital setting.