Accurate myocardial segmentation is crucial in the diagnosis and treatment of myocardial infarction (MI), especially in Late Gadolinium Enhancement (LGE) cardiac magnetic resonance (CMR) images, where the infarcted myocardium exhibits a greater brightness. However, segmentation annotations for LGE images are usually not available. Although knowledge gained from CMR images of other modalities with ample annotations, such as balanced-Steady State Free Precession (bSSFP), can be transferred to the LGE images, the difference in image distribution between the two modalities (i.e., domain shift) usually results in a significant degradation in model performance. To alleviate this, an end-to-end Variational autoencoder based feature Alignment Module Combining Explicit and Implicit features (VAMCEI) is proposed. We first re-derive the Kullback-Leibler (KL) divergence between the posterior distributions of the two domains as a measure of the global distribution distance. Second, we calculate the prototype contrastive loss between the two domains, bringing closer the prototypes of the same category across domains and pushing away the prototypes of different categories within or across domains. Finally, a domain discriminator is added to the output space, which indirectly aligns the feature distribution and forces the extracted features to be more favorable for segmentation. In addition, by combining CycleGAN and VAMCEI, we propose a more refined multi-stage unsupervised domain adaptation (UDA) framework for myocardial structure segmentation. We conduct extensive experiments on the MSCMRSeg 2019, MyoPS 2020 and MM-WHS 2017 datasets. The experimental results demonstrate that our framework achieves superior performances than state-of-the-art methods.

中文翻译：

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通过基于变分自动编码器的无监督域适应实现准确的心脏 MRI 分割


准确的心肌分割对于心肌梗死（MI）的诊断和治疗至关重要，特别是在晚期钆增强（LGE）心脏磁共振（CMR）图像中，梗塞心肌表现出更大的亮度。然而，LGE 图像的分割注释通常不可用。尽管从具有充足注释的其他模态的 CMR 图像（例如平衡稳态自由进动 (bSSFP)）获得的知识可以转移到 LGE 图像，但这两种模态之间的图像分布差异（即域偏移）通常会导致模型性能显着下降。为了缓解这一问题，提出了一种基于结合显式和隐式特征的特征对齐模块（VAMCEI）的端到端变分自动编码器。我们首先重新推导两个域的后验分布之间的 Kullback-Leibler (KL) 散度作为全局分布距离的度量。其次，我们计算两个领域之间的原型对比损失，使跨领域的同一类别的原型更加接近，并推开领域内或跨领域的不同类别的原型。最后，在输出空间中添加域鉴别器，间接对齐特征分布并强制提取的特征更有利于分割。此外，通过结合CycleGAN和VAMCEI，我们提出了一种更精细的多阶段无监督域适应（UDA）框架用于心肌结构分割。我们在 MSCMRSeg 2019、MyoPS 2020 和 MM-WHS 2017 数据集上进行了广泛的实验。实验结果表明，我们的框架比最先进的方法具有更优越的性能。