Accurate segmentation of cardiovascular magnetic resonance (CMR) images is an important pre-requisite in cardiology to reliably assess and diagnose several major cardiovascular diseases. Deep learning techniques represent nowadays the state-of-the-art in automatic CMR segmentation. However, these models are commonly trained and validated using datasets collected in single clinical centres or with homogeneous imaging protocols, limiting the development of models that are generalisable across different clinical centres or different scanners.
In this project, the participants will be trained to implement generalisable deep learning-based segmentation models using the python programming language. First, they will implement baseline convolutional neural networks trained with images from a single domain (e.g. from a single hospital) and validated on new unseen domains (i.e. in other hospitals). Second, they will implement several strategies such as data augmentation, domain adaptation or transfer learning to obtain generalisable models across hospitals, scanners and populations.
Mentor: Victor Campello