用噪声标签训练出鲁棒的心肌瘢痕分割模型，提升临床可用性。
Robust Deep Learning for Myocardial Scar Segmentation in Cardiac MRI with Noisy Labels
- 引入KL散度损失与数据增强，应对标注噪声和数据异质性。
- 在急性和慢性病例上均实现精准平滑分割，优于nnU-Net。
- 适合真实临床场景中存在标注不一致的医学图像分析。
从心脏MRI中准确分割心肌瘢痕对临床评估与治疗规划至关重要。本文提出一种鲁棒的深度学习流程，通过微调先进模型实现全自动心肌瘢痕检测与分割。该方法通过使用Kullback-Leibler损失及大规模数据增强，有效应对半自动标注带来的标签噪声、数据异质性及类别不平衡问题。我们在急性与慢性病例上评估模型性能，证明其在存在噪声标签的情况下仍能生成准确且平滑的分割结果。尤其在分布外测试集上，本方法显著优于nnU-Net等现有模型，展现出跨多种成像条件与临床任务的强泛化能力。这些结果为自动化心肌瘢痕量化奠定了可靠基础，推动深度学习在心脏影像中的更广泛应用。
原文摘要 · Abstract (English)
The accurate segmentation of myocardial scars from cardiac MRI is essential for clinical assessment and treatment planning. In this study, we propose a robust deep-learning pipeline for fully automated myocardial scar detection and segmentation by fine-tuning state-of-the-art models. The method explicitly addresses challenges of label noise from semi-automatic annotations, data heterogeneity, and class imbalance through the use of Kullback-Leibler loss and extensive data augmentation. We evaluate the model's performance on both acute and chronic cases and demonstrate its ability to produce accurate and smooth segmentations despite noisy labels. In particular, our approach outperforms state-of-the-art models like nnU-Net and shows strong generalizability in an out-of-distribution test set, highlighting its robustness across various imaging conditions and clinical tasks. These results establish a reliable foundation for automated myocardial scar quantification and support the broader clinical adoption of deep learning in cardiac imaging.
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