## 2 Background and Related Works
### Myocardial Infarction Segmentation  
Intensity-based segmentation algorithms have been widely investigated and validated in clinical practice. In these techniques, the histogram thresholding is conducted in a semi-automatic [4; 10; 22] or automatic approach [19; 23]. While the FWHM method [10] requires the manual identification of a hyper-enhanced zone, the n-SD [4] method requires the manual identification of a remote myocardial one. Given that these approaches cannot deal with the overlapping tissue distribution areas, several studies extended or combined them by using more sophisticated tools. Common works recombined the thresholding techniques [11; 24; 25] or used intensity features with connected component analysis [16; 20; 26], clustering [27; 28] or support-vector-machines (SVM) [29; 30]. Graph-cuts [17; 31], watershed [15; 32] and continuous max-flow [33; 34] algorithms have received researchers' attention as well. Currently, with the advent of GPUs, there is an increasing interest in deep-learning based strategies given their outstanding performance, speed and user-free operation. Under certain databases (such as large, multi-scanner and multi-center) and training considerations, convolutional neural networks (CNN) have shown robustness not only for handling image variability (coming from acquisition differences like voxel resolution, noise, artifacts, intensity distributions, etc.), but also for handling the inherent patient one (such as cardiac shape, location, and lesion configuration differences). In this regard, CNNs have been explored for segmentation of myocardial scars in 2D [35; 36] and 3D [37; 38; 39; 40] LGE-MRI sequences. In [35; 36], a 2D fully CNN based on the ENet [41] model was proposed for left ventricle scar segmentation. The best model found by the authors reached a \(\sim\)71% Dice coefficient over a 30-subject cohort. Segmentation of 3D LGE-MRI images was initially explored in [37] by means of a 2D patch-based CNN and was later extended to a 3D CNN model [38]. In their 3D outperforming proposal, the authors conducted a binary (_Healthy_ vs. _Diseased_) voxel-wise classification of the myocardial area using downsampled MRI images. A high \(\sim\)93% Dice coefficient was found over a 34-subject cohort. The work was later enhanced by including a left ventricle cavity segmentation model in the pipeline [42]. In [43], a deep learning fusion of LGE with cine MRI images was proposed for improving the scar segmentation performance. The segmentation of _atrial_ scars using deep learning has also been explored. In [39], a super-pixel feature-extraction algorithm followed by a binary classification with stacked sparse autoencoders was proposed. More recently, [PERSON] et al. [40] proposed a similar super-pixel approach where classification was performed with SVMs instead.  
Despite the vast techniques exploration, up to now there is no reference method for scar quantification [8] and mostly the threshold-based methods are implemented on clinical workstations. The considered reference approaches comprise the n-SD and FWHM, even when their variability, reproducibility and lack of expert agreement were highly discussed [12; 13]. For these reasons, the development of automatic approaches that could better reproduce the experts' delineations becomes highly valuable.