Deep Learning for fully automated cardiac tissue classification from dynamic cardiac MR and CT imaging

Recent advances in MR and CT enable a precise assessment of the severity of heart diseases by showing how well the heart muscle is perfused. This can help in the early diagnosis of these diseases, and in the better planning of therapy. In this project, LUMC and Medis will develop automated analysis software that helps the radiologist interpret the patient’s condition from these images.

Over the past 4 years, a novel technology known as “Deep Learning” has revolutionized the field of automated image analysis and object recognition. However, the amount and complexity of such dynamic MR and CT data requires “artificially intelligent” techniques that help the radiologist and cardiologist to interpret this data. We therefore aim to develop framework based on Deep Learning for tissue classification from these complex MR and CT scans. 

Ultimately, the proposed software will help the medical professional with early diagnosis (before the damage is done), with the selection of the proper treatment (intervention). This will contribute to more personalized therapy, with benefits for the patient and the medical professional.

See the webiste of Ikeb for more information about the project.