## 1 Introduction
### Ongoing Efforts and Related Work  
**Semi-Supervised Learning:** Semi-supervised learning (SSL) [13; 14] has experienced much research attention thanks to the increasing availability of large-scale unlabeled data. Semi-supervised learning aims to revamp the model performance by learning from a small portion of labeled data along with optimizing an additional unsupervised loss on a larger portion of unlabeled data, assumed to be sampled from similar distributions, depending on the type of information that needs to be captured from the unlabeled data. Commonly, the rationale of SSL is based on generative models and adversarial networks. The integration of consistency regularization in SSL has shed light on standard baselines recently. By optimizing this loss term, the model imposes several assumptions/constraints on the decision boundary to avoid high-density regions of unannotated data. **Generative adversarial networks:** Moreover, generative adversarial learning can be adapted to semi-supervised learning for semantic segmentation [15; 16; 17] as well as by generating pseudo pixel-level predictions [18; 19]. Adversarial networks use a critic to predict the pixel-level distribution of the data, which acts as an adversarial loss term with the goal to provide the generator with learnable useful visual features from the unlabeled data for medical image synthesis [20]. Nonetheless, learning high-dimensional data can be difficult. Autoencoders struggle with multi-modal data distributions, and generative models rely on computationally demanding models, which are especially difficult to train. **Mutual information estimation:** Recent work on representation learning has focused on mutual information estimation [21]. As mutual information maximization has been shown to be effective at capturing the salient attributes of data, being able to disentangle these attributes is another desirable property. For example, it may be beneficial to remove data attributes that are irrelevant to a given task, such as illumination conditions in object recognition. **Disentanglement learning:** Some newly introduced techniques have dedicated considerable attention to disentangle representation with generative modeling [22; 23]. In disentangled representation, information is represented as a collection of (independent) factors [24], each of which corresponds to a meaningful aspect of the data [25; 26]. A current line of research has argued that disentangled representations are beneficial for a variety of tasks, including (semi-)supervised learning of downstream tasks, few-shot learning [27], and exploratory medical data analysis. Additionally, these representations also make it easier for later processes to only use the relevant parts of the data as input. **Unpaired image-to-image translation:** Image-to-image translation was first proposed by [PERSON] et al. in [28] in their conditional GAN paper. Furthermore, CycleGAN [29] tackles the problem of the above paired image translation approach by introducing a cycle-consistency loss to retrieve the original images by exploiting a cycle of translation. Later work [30] improved CycleGAN from one-to-one mapping to multimodal image generation. Nevertheless, in medical applications, image synthesis without explicit anatomy design constrain may lead to volatile anatomical structures and artifacts. Moreover, these methods are not aimed at medical image segmentation. **Domain Adaptation:** Domain adaptation, a form of transfer learning, encodes the distribution knowledge from a certain source domain to a different but related target domain, and thus, alleviates the domain shift discrepancy in real world applications [31]. Various methods have been proposed, including style and content-disentanglement [32],and adversary based approaches [33; 34]. As described later, in this work, we disentangle the most interpretable segmentation-aware spatial (skeleton) information.