Left Ventricular Parameter Regression from Deep Feature Maps of a Jointly Trained Segmentation CNN

Abstract

Quantification of left ventricular (LV) parameters from cardiac MRI is important to assess cardiac condition and help in the diagnosis of certain pathologies. We present a CNN-based approach for automatic quantification of 11 LV indices: LV and myocardial area, 3 LV dimensions and 6 regional wall thicknesses (RWT). We use an encoder-decoder segmentation architecture and hypothesize that deep feature maps contain important shape information suitable to start an additional network branch for LV index regression. The CNN is simultaneously trained on regression and segmentation losses. We validated our approach on the LVQuan19 training dataset and found that our proposed CNN significantly outperforms a standard encoder regression CNN. The mean absolute error and Pearson correlation coefficient obtained for the different indices are respectively 190 mm$^2$ (96$\%$), 214 mm$^2$ (0.90$\%$), 2.99 mm (95$\%$) and 1.82 mm (71$\%$) for LV area, myocardial area, LV dimensions and RWT on a three-fold cross validation and 186 mm$^2$ (97$\%$), 222 mm$^2$ (0.88$\%$), 3.03 mm (0.95$\%$) and 1.67 mm (73$\%$) on a five-fold cross validation.