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.

翻译：晚期钆增强（LGE）心脏磁共振（CMR）成像被视为评估ST段抬高型心肌梗死（STEMI）患者梗死面积（IS）和微血管阻塞（MVO）的体内参考标准。然而，对这些心肌梗死严重程度标志物的精确量化仍具有挑战性且非常耗时。由于LGE分布模式可能相当复杂且难以从血池或心外膜脂肪中勾勒，因此LGE CMR图像的自动分割具有挑战性。在这项工作中，我们提出了一种由二维和三维卷积神经网络（CNN）组成的级联框架，能够以全自动方式计算心肌梗死的范围。通过在级联框架训练过程中人为生成二维CNN典型的分割误差，我们强制检测并修正二维分割误差，从而提升整个方法的分割精度。所提出的方法使用EMIDEC挑战赛的训练数据集，通过五折交叉验证进行训练和评估。我们进行了比较实验，结果表明我们的框架优于EMIDEC挑战赛中的现有最佳方法，以及2D和3D nnU-Net。此外，在广泛的消融研究中，我们展示了所提出的纠错型级联方法的优势。