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Abstract #2228

The Apprentice Surpasses the Master: Training a Neural Network for Cardiac Segmentation Using a Specialized Network and Indirectly Labeled Data

Markus J. Ankenbrand1, David Lohr1, Tobias Wech2, and Laura M. Schreiber1
1Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Würzburg, Germany, 2Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany

Training of neural networks for segmentation of CMR images requires large amounts of labeled data and network generalization is biased by training data characteristics. We used a specialized network to label a heterogeneous, publicly available dataset of 1140 cine images with known left-ventricular volumes. We evaluated the performance of this network using true and predicted volumes and trained another neural network on subjects with high prediction accuracy using extensive data augmentation. The resulting network outperforms the original one on the full dataset, even on subgroups where the original network fails, indicating great generalization and thus suitability for transfer learning applications.

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