用多阶段分割与级联分类提升心脏MRI分析精度
Multi-Stage Segmentation and Cascade Classification Methods for Improving Cardiac MRI Analysis
- 分阶段用U-Net和ResNet分割心室，再经高斯平滑优化
- 左心室Dice达0.974，右心室达0.947，分类平均准确率97.2%
- 适合需要高精度心脏病诊断的临床研究与算法开发者
心脏磁共振成像的分割与分类对心脏病诊断至关重要，但现有方法在准确性与泛化能力上仍存挑战。本研究提出一种基于深度学习的新方法，采用多阶段流程：先用U-Net与ResNet模型进行分割，再经高斯平滑处理，显著提升分割效果，左心室Dice系数达0.974，右心室达0.947。分类阶段采用级联深度学习分类器，可区分肥厚型心肌病、心肌梗死及扩张型心肌病，平均准确率达97.2%。该方法优于现有模型，在分割精度与分类性能上均有提升，具有临床应用潜力，但仍需在多种成像协议下进一步验证与解释。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。