An increasing number of studies are leveraging unsupervised cross-modality synthesis to mitigate the limited label problem in training medical image segmentation models. They typically transfer ground truth annotations from a label-rich imaging modality to a label-lacking imaging modality, under an assumption that different modalities share the same anatomical structure information. However, since these methods commonly use voxel/pixel-wise cycle-consistency to regularize the mappings between modalities, high-level semantic information is not necessarily preserved. In this paper, we propose a novel anatomy-regularized representation learning approach for segmentation-oriented cross-modality image synthesis. It learns a common feature encoding across different modalities to form a shared latent space, where 1) the input and its synthesis present consistent anatomical structure information, and 2) the transformation between two images in one domain is preserved by their syntheses in another domain. We applied our method to the tasks of cross-modality skull segmentation and cardiac substructure segmentation. Experimental results demonstrate the superiority of our method in comparison with state-of-the-art cross-modality medical image segmentation methods.

中文翻译：

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用于跨模态医学图像分割的解剖学正则化表示学习。

越来越多的研究正在利用无监督的跨模态合成来缓解训练医学图像分割模型中的有限标签问题。他们通常将地面实况注释从富含标签的成像方式转移到缺乏标签的成像方式，假设不同的方式共享相同的解剖结构信息。然而，由于这些方法通常使用体素/像素循环一致性来规范模态之间的映射，因此不一定保留高级语义信息。在本文中，我们提出了一种新颖的解剖学正则化表示学习方法面向细分的跨模态图像合成。它学习跨不同模态编码的共同特征，以形成共享的潜在空间，其中 1) 输入及其合成呈现一致的解剖结构信息，2) 一个域中的两个图像之间的变换通过它们在另一个域中的合成来保留。我们将我们的方法应用于跨模态头骨分割和心脏亚结构分割的任务。实验结果证明了我们的方法与最先进的跨模态医学图像分割方法相比的优越性。