  Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely
established to assess the viability of myocardial tissue of patients after
acute myocardial infarction (MI). We propose the Cascading Refinement CNN
(CaRe-CNN), which is a fully 3D, end-to-end trained, 3-stage CNN cascade that
exploits the hierarchical structure of such labeled cardiac data. Throughout
the three stages of the cascade, the label definition changes and CaRe-CNN
learns to gradually refine its intermediate predictions accordingly.
Furthermore, to obtain more consistent qualitative predictions, we propose a
series of post-processing steps that take anatomical constraints into account.
Our CaRe-CNN was submitted to the FIMH 2023 MYOSAIQ challenge, where it ranked
second out of 18 participating teams. CaRe-CNN showed great improvements most
notably when segmenting the difficult but clinically most relevant myocardial
infarct tissue (MIT) as well as microvascular obstructions (MVO). When
computing the average scores over all labels, our method obtained the best
score in eight out of ten metrics. Thus, accurate cardiac segmentation after
acute MI via our CaRe-CNN allows generating patient-specific models of the
heart serving as an important step towards personalized medicine.
