MOTION MODELLING & ANALYSIS GROUP

Artificial Intelligence for Medical Image Analysis

Resources

See below for links to code, data and educational resources which are available publicly.

Data

A multi-speaker dataset of real-time two-dimensional speech magnetic resonance images with articulator ground-truth segmentations, from Ruthven et al, Scientific Data, 2023

MRI data for weighted manifold alignment experiments, from Clough et al, IEEE T-PAMI, 2020.

Code and data for generating synthetic high resolution dynamic abdominal MRI, from Chen et al, IEEE TMI, 2017

Software

Code for training U-Net model for segmentation of articulators from speech MRI, from Ruthven et al, Scientific Data, 2023

Code for multi-class topology-informed segmentation, from Byrne et al, IEEE TMI 2022.

Code for segmentation of A4C echocardiography images, from Puyol-Antón et al, MICCAI ASMUS 2022.

Code for active training of physics-informed neural networks, from Arthurs et al, Journal of Computational Physics, 2021.

Code for topology-informed segmentation of MNIST data, from Clough et al, IEEE TPAMI 2020.

Educational resources

Tutorial on deep learning based segmentation of cardiac MR, from the British Chapter of the ISMRM workhop on MR(A)I 2019, by Esther Puyol-Antón

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