## 5 Discussion
Thus, high performance results were even obtained before conducting the segmentation refinement, achieving the coarse segmentation step a better agreement with the ground truth than the reference methods. When the deep-learning based refinement was included, a consistent and statistical significant improvement in segmentation agreement was achieved. Even more, the low Dice variance showed homogeneity and adaptability of the method to different myocardial lesions configurations. Since the strategy relies on un-constrained located hyper-enhanced voxels detection, it should allow quantification of not only subendocardial scars, but also of non-subendocardial LGE patterns as found in hypertrophic cardiomyopathy or myocarditis. The method showed, besides, a stable performance independent on the cardiac location considered. Myocardial scar segmentation at the base and the apex of the heart were slighter lesser than at the middle. Our hypothesis is that at these locations, the stronger partial volume effect turns tissue segmentation more challenging. The increased Hausdorff distances found from the myocardial apex to the base are related with the larger myocardial area involved and the higher chances of the method to provide misclassification of voxels. When assessing Hausdorff distance results, our method obtained much lower values than most reference algorithms (\(p<0.05\)). Only the 4-SD and 5-SD methods achieved lower metrics (p non-statistically significant), which is expected since in these algorithms the intensity segmentation threshold is set very high. Thus, only highly hyperenhanced voxels from the core necrotic tissue are detected and hence outliers (which are more prone to appear at lower thresholds) are avoided. As a consequence, these methods may have better Hausdorff distance results since this metric is strongly affected by outliers [49]. Promising results in terms of clinical markers were achieved with the proposed algorithm. The high correlation, low bias and the fact of being the only method agreeing in volumetric lesion quantification with the manual delineations suggest its appropriateness for potentially working under clinical and medical conditions. It is important to highlight that our results shared the intra- and inter-rater variability ranges. On the other hand, supporting the findings of [12; 13] the reference results showed very poor scar segmentation agreement with the manual delineations, characterized by low accuracies, high results variability and significant differences in volumetric tissue quantification. A strength of the dataset used is the _real world_ data included, which comprises large image quality variability and a wide range of disease severity. For instance, cases highly corrupted with artifacts or with very small myocardial lesions have not been excluded from our analysis. These dataset considerations could explain the large variability in the segmentation metrics reported in this work. Another novelty from our dataset is the inclusion of cases containing no-reflow areas. The MVO performance evaluation showed that our approach was consistently superior for conducting this task, achieving the highest sensitivity performance and evidencing statistical significance when compared against the reference approaches. The 1-SD method was the only exception, showing non-significant differences even when achieving lower performance. For this latter technique, the setting of a very low threshold for detecting myocardial scars favors MVO detection at the expenses of providing low overall performance. For evaluating the methods' robustness and generalizability, validation over large, public and multi-center databases is needed. As a limitation of our proposal we can point out the two independent modules devised for lesion detection and quantification. Future goals will address the algorithm unification into a unique block as well as the full segmentation of the left-ventricle myocardium.