  Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI)
is the gold standard technique for myocardial viability assessment. Although
the technique accurately reflects the damaged tissue, there is no clinical
standard for quantifying myocardial infarction (MI), demanding most algorithms
to be expert dependent. Objectives and Methods: 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-obstructed 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 CNNs. For its validation, reproducibility and further comparison
against other methods, we tested the method on a big multi-field expert
annotated LGE-MRI database including healthy and diseased cases. Results and
Conclusion: In an exhaustive comparison against nine reference algorithms, the
proposal achieved state-of-the-art segmentation performances and showed to be
the only method agreeing in volumetric scar quantification with the expert
delineations. Moreover, the method was able to reproduce the intra- and
inter-observer variability ranges. It is concluded that the method could
suitably be transferred to clinical scenarios.
