用深度学习无监督聚类心肌纤维轨迹，精准区分复杂结构。
Deep Representation Learning for Unsupervised Clustering of Myocardial Fiber Trajectories in Cardiac Diffusion Tensor Imaging
- 融合Bi-LSTM与Transformer，捕捉纤维局部与全局特征
- 聚类出33至62个稳定簇，实现细粒度纤维结构划分
- 无需标注，适合心脏疾病研究与个性化治疗分析
理解复杂的心肌结构对心脏病的诊断与治疗至关重要。然而，现有方法在从弥散张量成像（DTI）数据中准确捕捉这种精细结构时面临挑战，主要源于缺乏真实标签以及纤维轨迹的模糊与交织特性。本文提出一种新型深度学习框架，用于心肌纤维的无监督聚类，提供一种数据驱动的方法来识别不同的纤维束。该框架创新性地结合双向长短期记忆网络（Bi-LSTM）以捕获沿纤维的局部序列信息，以及基于Transformer的自编码器以学习全局形状特征，并融入点级解剖上下文信息。利用密度基算法对这些表示进行聚类，成功识别出33至62个稳健簇，有效捕捉了不同粒度下的纤维轨迹细微差异。该框架为心肌结构分析提供了新的、灵活且定量的方法，在已知文献中尚未实现如此细致的划分，具有改进手术规划、表征疾病相关重构及推动个性化心脏医疗的潜力。
原文摘要 · Abstract (English)
Understanding the complex myocardial architecture is critical for diagnosing and treating heart disease. However, existing methods often struggle to accurately capture this intricate structure from Diffusion Tensor Imaging (DTI) data, particularly due to the lack of ground truth labels and the ambiguous, intertwined nature of fiber trajectories. We present a novel deep learning framework for unsupervised clustering of myocardial fibers, providing a data-driven approach to identifying distinct fiber bundles. We uniquely combine a Bidirectional Long Short-Term Memory network to capture local sequential information along fibers, with a Transformer autoencoder to learn global shape features, with pointwise incorporation of essential anatomical context. Clustering these representations using a density-based algorithm identifies 33 to 62 robust clusters, successfully capturing the subtle distinctions in fiber trajectories with varying levels of granularity. Our framework offers a new, flexible, and quantitative way to analyze myocardial structure, achieving a level of delineation that, to our knowledge, has not been previously achieved, with potential applications in improving surgical planning, characterizing disease-related remodeling, and ultimately, advancing personalized cardiac care.
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