Summary
I am interested in using Magnetic Resonance Imaging (MRI), digital twins and AI to understand and improve the treatment of cardiac disease.
Some of the questions I am currently investigating are:
- Can we use machine learning (neural networks) to infer the properties of the heart from measurements of electrical potential at its surface? For this, we use Physics-Informed Machine Learning (read our latest paper).
- How do the atria deform is the propagation of electrical signals in the heart affected by the mechanical deformations it undergoes? (New paper coming out soon!)
- What clinical information can we extract from cardiac MRI with the help of machine learning? (Read our latest example on automatically quantifying fat around the heart.)
If you are a non-scientist, you may be interest in our app to demonstrate how you may be able to treat cardiac arrhythmias (Android only).
Publications
Journals
Lalande A, Chen Z, Pommier T, et al., 2022, Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge, Medical Image Analysis, Vol:79, ISSN:1361-8415
Varela M, Roy A, Lee J, 2022, A survey of pathways for mechano-electric coupling in the atria (vol 159, pg 136, 2021), Progress in Biophysics & Molecular Biology, Vol:169-170, ISSN:0079-6107, Pages:94-94
Bharath A, Uslu F, Varela Anjari M, et al., 2022, LA-Net: A multi-task deep network for the segmentation of the left atrium, Ieee Transactions on Medical Imaging, Vol:41, ISSN:0278-0062, Pages:456-464
Li Z, Petri C, Howard J, et al., 2022, PAT-CNN: Automatic Segmentation and Quantification of Pericardial Adipose Tissue from T2-Weighted Cardiac Magnetic Resonance Images, Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol:13593 LNCS, ISSN:0302-9743, Pages:359-368
Conference
Galazis C, Wu H, Li Z, et al., 2022, Tempera: Spatial Transformer Feature Pyramid Network for Cardiac MRI Segmentation, Pages:268-276, ISSN:0302-9743