Error correcting 2D-3D cascaded network for myocardial infarct scar
  segmentation on late gadolinium enhancement cardiac magnetic resonance images
Matthias Schwab Mathias Pamminger Christian Kremser Daniel Obmann Markus Haltmeier Agnes Mayr
Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is
considered the in vivo reference standard for assessing infarct size (IS) and
microvascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI)
patients. However, the exact quantification of those markers of myocardial
infarct severity remains challenging and very time-consuming. As LGE
distribution patterns can be quite complex and hard to delineate from the blood
pool or epicardial fat, automatic segmentation of LGE CMR images is
challenging. In this work, we propose a cascaded framework of two-dimensional
and three-dimensional convolutional neural networks (CNNs) which enables to
calculate the extent of myocardial infarction in a fully automated way. By
artificially generating segmentation errors which are characteristic for 2D
CNNs during training of the cascaded framework we are enforcing the detection
and correction of 2D segmentation errors and hence improve the segmentation
accuracy of the entire method. The proposed method was trained and evaluated in
a five-fold cross validation using the training dataset from the EMIDEC
challenge. We perform comparative experiments where our framework outperforms
state-of-the-art methods of the EMIDEC challenge, as well as 2D and 3D nnU-Net.
Furthermore, in extensive ablation studies we show the advantages that come
with the proposed error correcting cascaded method.