The segmentation and classification of cardiac magnetic resonance imaging are critical for diagnosing heart conditions, yet current approaches face challenges in accuracy and generalizability. In this study, we aim to further advance the segmentation and classification of cardiac magnetic resonance images by introducing a novel deep learning-based approach. Using a multi-stage process with U-Net and ResNet models for segmentation, followed by Gaussian smoothing, the method improved segmentation accuracy, achieving a Dice coefficient of 0.974 for the left ventricle and 0.947 for the right ventricle. For classification, a cascade of deep learning classifiers was employed to distinguish heart conditions, including hypertrophic cardiomyopathy, myocardial infarction, and dilated cardiomyopathy, achieving an average accuracy of 97.2%. The proposed approach outperformed existing models, enhancing segmentation accuracy and classification precision. These advancements show promise for clinical applications, though further validation and interpretation across diverse imaging protocols is necessary.

翻译：心脏磁共振成像的分割与分类对于心脏疾病的诊断至关重要，然而现有方法在准确性与泛化能力方面仍面临挑战。本研究旨在通过引入一种新颖的基于深度学习的方法，进一步推进心脏磁共振图像的分割与分类。该方法采用包含U-Net与ResNet模型的多阶段分割流程，并结合高斯平滑处理，从而提升了分割精度，实现了左心室分割Dice系数0.974、右心室分割Dice系数0.947的结果。在分类任务中，采用级联深度学习分类器对包括肥厚型心肌病、心肌梗死及扩张型心肌病在内的心脏疾病进行鉴别，平均准确率达到97.2%。所提出的方法在分割精度与分类准确率上均优于现有模型。这些进展显示出良好的临床应用前景，但仍需在不同成像协议下进行进一步的验证与解释。