Fully automatic extraction of mitral valve annulus motion parameters on long-axis CINE CMR using deep learning
May 2021·
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0 min read
Maria Monzon
Yoon S.S.
Fischer C.
Maier A.
Wetzl J.
Giese D.
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.
Type
Publication
International Society for Magnetic Resonance in Medicine (ISMRM), 29th Annual Meeting
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.