Multilevel Comparison of Neural Networks for Ventricular Function Quantification in CMR accelerated by Compressed Sensing
Thomas Hadler1, Clemens Ammann1, Jan Gröschel1,2, and Jeanette Schulz-Menger1,2,3
1Charité - Universitätsmedizin Berlin, Working Group on CMR, Experimental and Clinical Research Center, a joint cooperation between the Max-Delbrück-Center for Molecular Medicine and the Charité – Universitätsmedizin Berlin, Berlin, Germany, 2DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany, 3Department of Cardiology and Nephrology, Helios Hospital Berlin-Buch, Berlin, Germany
Three popular convolutional neural networks were trained on short-axis cine MR images of the heart acquired by a prototype 2-shot 2D Compressed Sensing sequence. Network performance was evaluated on the level of clinical results and segmentation quality. Analysis revealed high correlation for quantitative results between all networks and a human expert. Automatic segmentation of the right ventricle is significantly more difficult than the left ventricle and shows more outliers. Segmentation decision errors concentrate in basal and apical slices, with the largest millilitre differences in the basal slices. Fast acquisition and automated image analysis promise high efficiency in CMR.
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