###### Abstract  
Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (_u_ = 20) and diseased (_u_ = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 \(\pm\) 14.3% and with a volumetric error of 1.0 \(\pm\) 6.9 cm\({}^{3}\). In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (_p_ \(<\) 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed. c 2021 17 August 2021 20 August 2021 20 August 2021 20 August 2021 therapy fails, severe and extensive damage of the myocardium might be observed due to the permanent microvascular obstruction (MVO, also known as _no-reflow_) phenomenon [3; 5; 6]. The accurate diagnosis of the MVO is important since it evidences the lack of reperfusion of some myocardial area even after the ending of the ischemic event, indicating severe ischemic disease and being associated with poor prognosis, adverse left ventricular remodeling, adverse cardiac events and death [7]. The main limitations of LGE-MRI for myocardial tissue assessment are not only due to technical parameters setting (such as slice thickness, inversion recovery, etc. ), but mainly due to the lack of a clinical standard for scar tissue quantification [8; 9]. Thus, nowadays there is no reference method for abnormal tissue detection and segmentation, even though several techniques have been explored. The most frequently used techniques are the threshold-based ones, such as the full-width at half-maximum (FWHM) [10] and the n-standard deviations (from now on n-SD) [4]. Although [PERSON] et al. [11] obtained good performance and reproducibility for the FWHM method, the works of [PERSON] et al. [12] and [PERSON] et al. [13] reported controversial results for both techniques. While manual delineations have the drawback of being subjective [8], threshold-based methods are prone to inaccuracies, suffer from intra- and inter-rater variation [14], and may lead to false-positive detections when operating over healthy images. As such, these methods \((i)\) require visual identification of diseased myocardium before quantifying the lesions and \((ii)\) can only detect the hyper-enhanced scar tissue since MVO areas are not addressed. In this work, a new LGE-MRI infarction quantification method is devised.