Calculation of Anatomical and Functional Metrics Using Deep Learning in Cardiac MRI: Comparison Between Direct and Segmentation-Based Estimation

In this paper we propose a collection of left ventricle (LV) quantification methods using different versions of a common neural network architecture. In particular, we compare the accuracy obtained with direct calculation (regression) of the desired metrics, a segmentation network and a novel combined approach. We also introduce temporal dynamics through the use of a Long Short-Term Memory (LSTM) network. We train and evaluate our methods on MICCAI 2018 Left Ventricle Full Quantification Challenge dataset. We perform 5-fold cross-validation on the training dataset and compare our results with the state-of-the-art methods evaluated on the same dataset. In our experiments, segmentation-based methods outperform all the other variants as well as current state of the art. The introduction of LSTM does produces only minor improvements in accuracy. The novel segmentation/estimation network improves the results on estimation-only but does not reach the accuracy of segmentation-based metric calculation.