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Abstract #2664

Fully automatic extraction of mitral valve annulus motion parameters on long axis CINE CMR using deep learning

Maria Monzon1,2, Seung Su Yoon1,2, Carola Fischer2, Andreas Maier1, Jens Wetzl2, and Daniel Giese2
1Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany

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.

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