Transfer Learning in Ultrahigh Field (7T) Cardiac MRI – Automatic Left Ventricular Segmentation in a Porcine Animal Model
Alena Kollmann1, David Lohr1, Markus Ankenbrand2, Maya Bille1, Maxim Terekhov1, Michael Hock1, Ibrahim Elabyad1, Theresa Reiter1,3, Florian Schnitter3, Wolfgang Bauer1,3, Ulrich Hofmann3, and Laura Schreiber1
1Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Wuerzburg, Wuerzburg, Germany, 2Center for Computational and Theoretical Biology (CCTB), University of Wuerzburg, Wuerzburg, Germany, 3Department of Internal Medicine I, University Hospital Wuerzburg, Wuerzburg, Germany
Cardiac magnetic resonance (CMR) is considered the gold standard for evaluating cardiac function. Tools for automatic segmentation already exist in the clinical context. To bring the benefits of automatic segmentation to preclinical research, we use a deep learning based model. We demonstrate that for training small data sets with parameters deviating from the clinical situation (high resolution images of porcine hearts acquired at 7T in this case) are sufficient to achieve good correlation with manual segmentation. We obtain DICE-scores of 0.87 (LV) and 0.85 (myocardium) and find high agreement of the calculated functional parameters (Pearson‘s r between 0.95 and 0.99).
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