This work presents a novel framework CISFA (Contrastive Image synthesis and Self-supervised Feature Adaptation)that builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. Different from existing works, we use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image, which serves as shape constraints. Moreover, we notice that the generated images and input images share similar structural information but are in different modalities. As such, we enforce contrastive losses on the generated images and the input images to train the encoder of a segmentation model to minimize the discrepancy between paired images in the learned embedding space. Compared with existing works that rely on adversarial learning for feature adaptation, such a method enables the encoder to learn domain-independent features in a more explicit way. We extensively evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts. Experimental results show that the proposed framework not only outputs synthetic images with less distortion of organ shapes, but also outperforms state-of-the-art domain adaptation methods by a large margin.

翻译：本文提出了一种新颖的框架CISFA（对比图像合成与自监督特征适应），该框架基于图像域转换和无监督特征适应，用于跨模态生物医学图像分割。与现有工作不同，我们采用单侧生成模型，并在输入图像的采样块与相应合成图像之间添加加权块级对比损失，作为形状约束。此外，我们注意到生成图像与输入图像共享相似的结构信息但处于不同模态。因此，我们在生成图像和输入图像上施加对比损失，以训练分割模型的编码器，从而最小化配对图像在所学嵌入空间中的差异。与现有依赖对抗学习进行特征适应的方法相比，该方法使编码器能够以更显式的方式学习域无关特征。我们在包含腹部腔体和全心的CT及MRI图像分割任务上对方法进行了广泛评估。实验结果表明，所提框架不仅生成器官形状畸变更小的合成图像，而且在域自适应性能上大幅超越当前最先进方法。