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Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging isconsidered the in vivo reference standard for assessing infarct size (IS) andmicrovascular obstruction (MVO) in ST-elevation myocardial infarction (STEMI)patients. However, the exact quantification of those markers of myocardialinfarct severity remains challenging and very time-consuming. As LGEdistribution patterns can be quite complex and hard to delineate from the bloodpool or epicardial fat, automatic segmentation of LGE CMR images ischallenging. In this work, we propose a cascaded framework of two-dimensionaland three-dimensional convolutional neural networks (CNNs) which enables tocalculate the extent of myocardial infarction in a fully automated way. Byartificially generating segmentation errors which are characteristic for 2DCNNs during training of the cascaded framework we are enforcing the detectionand correction of 2D segmentation errors and hence improve the segmentationaccuracy of the entire method. The proposed method was trained and evaluated ina five-fold cross validation using the training dataset from the EMIDECchallenge. We perform comparative experiments where our framework outperformsstate-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 comewith the proposed error correcting cascaded method.

Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images