On the Relevance of the Loss Function in the Agatston Score Regression from Non-ECG Gated CT Scans.

In this work, we evaluate the relevance of the choice of loss function in the regression of the Agatston score from 3D heart volumes obtained from non-contrast non-ECG gated chest computed tomography scans. The Agatston score is a well-established metric of cardiovascular disease, where an index of coronary artery disease (CAD) is computed by segmenting the calcifications of the arteries and multiplying each calcification by a factor related to their intensity and their volume, creating a final aggregated index. Recent work has automated such task with deep learning techniques, even skipping the segmentation step and performing a direct regression of the Agatston score. We study the effect of the choice of the loss function in such methodologies. We use a large database of 6983 CT scans to which the Agatston score has been manually computed. The dataset is split into a training set and a validation set of n = 1000. We train a deep learning regression network using such data with different loss functions while keeping the structure of the network and training parameters constant. Pearson correlation coefficient ranges from 0.902 to 0.938 depending on the loss function. Correct risk group assignment measurements range between 59.5% and 81.7%. There is a trade-off between the accuracy of the Pearson correlation coefficient and the risk group measurement, which leads to optimize for one or the other.