Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning
Proposed CNN system. The long-axis CMR images are forwarded to the first CNN which localizes the region of interest. After cropping and rotation, the second CNN regresses the time-resolved mitral valve annulus landmarks from Gaussian heatmaps. Finally, the motion parameters are extracted.
Abstract
The analysis of mitral valve motion is known to be relevant in the diagnosis of cardiac dysfunction. Dynamic motion parameters can be extracted from Cardiac Magnetic Resonance (CMR) images. We propose two chained Convolutional Neural Networks for automatic tracking of mitral valve-annulus landmarks on time-resolved 2-chamber and 4-chamber CMR images. The first network is trained to detect the region of interest and the second to track the landmarks along the cardiac cycle. We successfully extracted several motion-related parameters with high accuracy as well as analyzed unlabeled datasets, thereby overcoming time-consuming annotation and allowing statistical analysis over large number of datasets
Maria Monzon
Computer Vision and AI researcher
My research interests include Computer Vision, Biomedical Image Analysis, Trustworhty Deep Learning and Machine Learning for healthcare.