多疾病感知训练策略用于心脏磁共振图像分割
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
AI总结：
针对心脏MRI分割中右心室等不规则器官性能差的问题，提出多疾病感知训练策略（MTS）重构多疾病数据集并配合专门预处理，实验证明该方法提升分割性能且对未知疾病数据具有鲁棒性。
摘要：
从心脏磁共振图像（CMRI）中准确分割心室对于增强心脏疾病的诊断和分析至关重要。基于深度学习的分割方法近年来因其出色的性能而受到广泛关注。然而，这些分割方法通常擅长分割形状规则的器官，如左心室（LV）和心肌（MYO），而在分割形状不规则的器官（如右心室（RV））时表现不佳。在本研究中，我们认为分割模型的这一局限性源于其泛化能力不足，无法应对分割目标在切片、心脏相位和疾病状态间的分布偏移。为解决此问题，我们提出了一种多疾病感知训练策略（MTS），并将引入的CMRI数据集重构为多疾病数据集。此外，我们提出了一种专门的数据处理技术，用于预处理输入图像以支持MTS。为验证我们方法的有效性，我们进行了对照组实验和交叉验证测试。实验结果表明：（1）使用我们提出的策略训练的网络模型取得了更优的分割性能，尤其是在右心室分割方面；（2）这些网络在应用于未知疾病的数据时也表现出稳健的性能。
英文摘要：
Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases.
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