Mitral Valve Annulus Motion Parameter Extraction

Jan 2021 · 1 min read
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Diastolic dysfunction is an important cause of cardiac insuf- ficiency, defined as a malfunctioning filling of the heart during diastole. The analysis of mitral valve motion is known to be relevant in the diagno- sis of cardiac dysfunction. Cardiac motion parameters can be extracted from Cardiac Magnetic Resonance (CMR) images. However, in clinical setting valve motion modeling usually needs a manual intervention to lo- calize the valvular plane. We propose two chained Convolutional Neural Networks (CNN) for automatic tracking of mitral valve-annulus land- marks on time-resolved 2-chamber and 4-chamber CMR images. The first CNN is trained to detect the region of interest and the second to track the landmarks along the cardiac cycle. The presented deep learn- ing system has high accuracy in terms of temporal landmark tracking and motion assessment. Furthermore, we successfully extracted several motion-related parameters thereby overcoming time-consuming annota- tion and allowing statistical analysis over a large number of datasets.

Medical-Imaging Deep-Learning
Maria Monzon
Authors
Maria Monzon (she/her)
Computer Vision & Medical AI Researcher
PhD candidate at ETH Zurich developing robust and trustworthy deep learning for medical image analysis — spine and cardiac MRI, multimodal biomedical data, and uncertainty quantification. Previously a computer-vision researcher at BASF, where I deployed models to production in regulated, GLP-certified environments. I care about efficient code and reproducible research.