MOTION MODELLING & ANALYSIS GROUP
Artificial intelligence for medical image analysis
The Motion Modelling and Analysis Group (MMAG) is an academic research group based within the School of Biomedical Engineering and Imaging Sciences of King's College London. The group is based at St. Thomas' Hospital in central London. The MMAG includes researchers from a diverse range of backgrounds and is proud to foster an inclusive research environment to allow all to thrive and reach their potential.
The MMAG has worked in the past on the imaging, modelling and estimation of repetitive motion. Currently, the group works more widely on the use of artificial intelligence (specifically machine and deep learning) for the analysis of imaging data with the aim of extracting clinically useful information. Cardiology is one of the main application areas but the group is also involved in developing machine learning solutions for other clinical application areas, such as radiotherapy and vocal tract imaging. The aim is to develop novel but clinically-driven machine learning solutions and translate them into equitable patient benefit.
6 November 2022
New journal paper by M. Ruthven, et al, "A Segmentation-informed Deep Learning Framework to Register Dynamic Two-dimensional Magnetic Resonance Images of the Vocal Tract During Speech", Biomedical Signal Processing and Control, 2022. (paper)
8 September 2022
New MICCAI workshop papers now have Arxiv papers online:
G. Morilhat, N. Kifle, S. FinesilverSmith, B. Ruijsink, V. Vergani, Habtamu T. D., Zerubabel T. D., E. Puyol-Antón, A. Carass, A. P. King, "Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions", Proceedings MICCAI FAIR, 2022. (Arxiv paper)
S. Ioannou, H. Chockler, A. Hammers, A. P. King, "A Study of Demographic Bias in CNN-based Brain MR Segmentation", Proceedings MICCAI MLCN, 2022. (Arxiv paper)
L. Humbert-Vidan, V. Patel, R. Andlauer, A. P King, T. G. Urbano, "Prediction of Mandibular ORN Incidence From 3D Radiation Dose Distribution Maps Using Deep Learning", Proceedings MICCAI AMAI, 2022. (Arxiv paper)
T. Lee, E. Puyol-Antón, B. Ruijsink, M. Shi, A. P. King, "A Systematic Study of Race and Sex Bias in CNN-based Cardiac MR Segmentation", Proceedings MICCAI STACOM, 2022. (Arxiv paper)
E. Puyol-Antón, B. Ruijsink, B. S. Sidhu, J. Gould, B. Porter, M. K. Elliott, V. Mehta, H. Gu, C. A. Rinaldi, M. Cowie, P. Chowienczyk, R. Razavi, A. P. King, "AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography", Proceedings MICCAI ASMUS, 2022. (Arxiv paper)
E. Chan, C. O'Hanlon, C. Asegurado Marquez, M. Petalcorin, J. Mariscal-Harana, H. Gu, R. J. Kim, R. M. Judd, P. Chowienczyk, J. A. Schnabel, R. Razavi, A. P. King, B. Ruijsink, E. Puyol-Antón, "Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging", Proceedings MICCAI STACOM, 2022. (Arxiv paper)
27 April 2022
New journal paper by E. Puyol-Antón, et al, "A Multimodal Deep Learning Model for Cardiac Resynchronisation Therapy Response Prediction", Medical Image Analysis, 2022. (open access paper)