2021-2022 PRojects

The objective of this project is to automatically determine congenital heart conditions from phase-contrast heart MRIs and their correlation with long-term clinical outcomes. We use data from the UK biobank. Currently, we are working on using the long-axis view from cardiac MRI sequences to detect mitral valve regurgitation. The framework we introduced includes a sequence classification model, an image segmentation model and an ensemble model. Given that the severity of mitral valve regurgitation varies and the pathology is visible across 3 different views of cardiac MRI sequences, we are working towards a multi-view sequence classification model for detecting mitral valve regurgitation. We also use weak supervision for heart chamber segmentation. To better use the information contained in the images, we are developing weakly-supervised models to segment the unlabeled datasets from the UK Biobank. We are working toward a weakly-supervised or unsupervised solution to extract salient information from cardiac MRI sequences, which will be useful in other medical imaging applications.

The prerequisites are:

• intermediate programing skills

• logical reasoning ability

• mathematics skills (basic linear algebra, calculus, statistics and probability)

Here are some examples of skills you will gain:

• how to design and implement models for medical image segmentation and disease diagnosis/prediction

• how to appropriately evaluate machine learning models (how to design experiments, select test sets, perform cross-validation)

• how to optimize models, how to efficiently train and test them