Semantic segmentation of cardiac MR images is a challenging task due to its importance in medical assessment of heart diseases. Having a detailed localization of specific regions of interest such as Right and Left Ventricular Cavities and Myocardium, doctors can infer important information about the presence of cardiovascular diseases, which are today a major cause of death globally. This paper addresses the problem of semantic segmentation in cardiac MR images using a dilated Convolutional Neural Network. Opting for dilated convolutions allowed us to work in full resolution throughout the network's layers, preserving localization accuracy, while maintaining a relatively small number of trainable parameters. To assist the network's training process we designed a custom loss function. Furthermore, we developed new augmentation techniques and also adapted existing ones, to cope for the lack of sufficient training images. Consequently, the training set increases not only by amount, but by substance as well, and the network trains quickly and efficiently without overfitting. Our pre- and post-processing steps are also crucial to the whole process. We apply our methodology for the Right and Left Ventricles (RV, LV) and also the Myocardium (MYO) according to the Automated Cardiac Diagnosis Challenge (ACDC) with promising results. Submitting our algorithm's predictions to the Post-2017-MICCAI-challenge testing phase, we achieved similar scores (average Dice coefficient 0.916) on the test data set compared to the state of the art featured in the ACDC leaderboard, but with significantly fewer parameters than the leading method. Our approach outperforms other methods featuring dilated convolutions in this challenge up until now.

中文翻译：

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使用包含域特定约束的扩张 CNN 进行心脏 MRI 分割

由于其在心脏病医学评估中的重要性，心脏 MR 图像的语义分割是一项具有挑战性的任务。通过详细定位特定感兴趣区域（例如左右心室腔和心肌），医生可以推断出有关心血管疾病存在的重要信息，而心血管疾病如今已成为全球主要的死亡原因。本文使用扩张的卷积神经网络解决了心脏 MR 图像中的语义分割问题。选择扩张卷积使我们能够在整个网络层中以全分辨率工作，保持定位精度，同时保持相对较少的可训练参数。为了协助网络的训练过程，我们设计了一个自定义损失函数。此外，我们开发了新的增强技术并调整了现有技术，以应对缺乏足够训练图像的问题。因此，训练集不仅增加了数量，而且增加了实质，并且网络快速有效地训练而不会过度拟合。我们的预处理和后处理步骤对整个过程也至关重要。我们根据自动心脏诊断挑战 (ACDC) 将我们的方法应用于右心室和左心室 (RV、LV) 以及心肌 (MYO)，并取得了可喜的结果。将我们的算法预测提交到 Post-2017-MICCAI-challenge 测试阶段，与 ACDC 排行榜中的最新技术相比，我们在测试数据集上获得了相似的分数（平均 Dice 系数 0.916），但参数明显少于领先的方法。