## 5 Discussion  
In this work, a new method for myocardial infarction detection and quantification in LGE-MRI is presented. Our proposal differs in several ways from the existing deep-learning methods. Unlike most approaches, we did not use deep-learning for segmenting the entire scar, but for refining an initial coarse segmentation. Namely, we targeted with CNNs the reclassification of voxels falling in the boundary scar area, since partial volume effect and the gray-zone tissue turn their identification difficult. In this work, we assumed that voxels belonging to the core infarction are mostly distinguishable with intensity features, while those overlapping with healthy intensities require more complex descriptors for their classification. Moreover, our method takes advantage of CNNs for identifying diseased images before segmenting them. While the remaining approaches conduct direct lesion segmentation, we preferred to mimic the experts working pipeline by constraining the algorithms' lesion search to infarcted slices only. Another difference between deep-learning methods is the preference of 3D CNNs over 2D ones in 3D LGE-MRI images. This is in fact expected given the high voxel resolution and the free-breathing acquisition of 3D LGE-MRI. In 2D breath-holds imaging, slices are sequentially acquired with poor Z-axis resolution. As a consequence, the usage of 3D CNNs might be misleading since heart shape and location might differ in each breath-hold given diaphragm shifts. Despite the advantages of free-breathing 3D LGE-MRI, this imaging modality is not commonly used in clinical practice. A common prerequisite found for all methods segmenting the left ventricle scar is the manual myocardial delineation. In [36], a direct segmentation protocol without making use of myocardial masks was explored. However, the approach obtained much lower performance with a \(\sim\)17% Dice drop when compared with a model that uses myocardial boundaries. Among the main novelties of our proposal when compared against the existing methods we found: (_i_) the generalization of the algorithm for working under healthy scenarios, (_ii_) the automated detection of myocardial lesions and subsequent quantification, (_iii_) the incorporation of a dedicated step for including MVO areas within the scar segmentation and (_iv_) the validation of the algorithm on an LGE-MRI annotated database with both hyper-enhanced and MVO cases. All these characteristics make our proposal a highly valuable tool with clinical transfer potential. With the aim of overcoming most current algorithms' limitations for working with healthy images, a binary classifier for discriminating healthy and diseased cases was devised. The discriminant rule achieved high classification performance results. Our results suggest that features extracted with a CNN followed by a supervised classifier such as SVM performs better than an end-to-end training of CNN models for classifying myocardial images. When the decision rule was assessed in terms of a ROC analysis, high AUC metrics with low variance were obtained, suggesting robustness of the proposed discriminant rule. Possible operative points providing high sensitivity were proposed, which will help in reducing the false positive lesions' detection in healthy images. Even more, results from the permutation analysis showed that the built classifier and the features used are informative for the addressed problem and cannot be achieved by a randomchance configuration. All these findings support, consequently, the classifiers robustness as well as the method's reproducibility over different databases. Segmentation of the infarcted masses was conducted in a two-step approach where the initial segmentation was later improved using deep learning. It is important to highlight the novelty of this approach which was not only thought as a high-performance algorithm, but also as a modular transferable technique.