  A "heart attack" or myocardial infarction (MI), occurs when an artery
supplying blood to the heart is abruptly occluded. The "gold standard" method
for imaging MI is Cardiovascular Magnetic Resonance Imaging (MRI), with
intravenously administered gadolinium-based contrast (late gadolinium
enhancement). However, no "gold standard" fully automated method for the
quantification of MI exists. In this work, we propose an end-to-end fully
automatic system (MyI-Net) for the detection and quantification of MI in MRI
images. This has the potential to reduce the uncertainty due to the technical
variability across labs and inherent problems of the data and labels. Our
system consists of four processing stages designed to maintain the flow of
information across scales. First, features from raw MRI images are generated
using feature extractors built on ResNet and MoblieNet architectures. This is
followed by the Atrous Spatial Pyramid Pooling (ASPP) to produce spatial
information at different scales to preserve more image context. High-level
features from ASPP and initial low-level features are concatenated at the third
stage and then passed to the fourth stage where spatial information is
recovered via up-sampling to produce final image segmentation output into: i)
background, ii) heart muscle, iii) blood and iv) scar areas. New models were
compared with state-of-art models and manual quantification. Our models showed
favorable performance in global segmentation and scar tissue detection relative
to state-of-the-art work, including a four-fold better performance in matching
scar pixels to contours produced by clinicians.
