# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 5 Conclusions  
In this paper, we propose a novel cross-domain fundus image segmentation framework, Causal Self-Supervised Network (CSSN). Our approach constructs a structural causal model and introduces a causal intervention mechanism to effectively eliminate the interference of domain style information in pseudo-label generation, thereby enabling efficient self-supervised learning. Specifically, we employ a Fourier transform to convert the image style, simulating the stratification process inherent in backdoor adjustment. Subsequently, a dual-path segmentation network is used to separately extract features from the original and style-transformed images, and a confidence fusion strategy is leveraged to generate more reliable pseudo-labels. Furthermore, we incorporate adversarial training and cross-domain contrastive learning to effectively narrow the feature discrepancy between different domains, significantly enhancing the model's generalization ability and segmentation performance. Q.L. : Conceptualization, Methodology, and Writing--review & editing; Q.Z. : Software and Writing--original draft; Z.Z. : Investigation; H.L. : Validation, Supervision, and Writing--review & editing; W.N. : Funding acquisition. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (62272337), the Natural Science Foundation of Tianjin (16 JCZDJC31100) and Tianjin Natural Science Foundation (No. 23 JCQNJC01520). Not applicable. The code supporting the findings of this study is publicly available at the following GitHub repository: [[https://github.com/SingularZh/SSCN-for-OD-and-OC-segmentation.git](https://github.com/SingularZh/SSCN-for-OD-and-OC-segmentation.git)]([https://github.com/SingularZh/SSCN-for-OD-and-OC-segmentation.git](https://github.com/SingularZh/SSCN-for-OD-and-OC-segmentation.git)). The REFUGE dataset analyzed in this work can be accessed via its DOI: [[https://doi.org/10.21227/tz6e-r977](https://doi.org/10.21227/tz6e-r977)]([https://doi.org/10.21227/tz6e-r977](https://doi.org/10.21227/tz6e-r977)). The RIM-ONE-r3 dataset used herein is available for download at: [[https://mediumrg.webs.ull.es/RIM-ONE-r3.zip](https://mediumrg.webs.ull.es/RIM-ONE-r3.zip)]([https://mediumrg.webs.ull.es/RIM-ONE-r3.zip](https://mediumrg.webs.ull.es/RIM-ONE-r3.zip)). The authors declare no conflicts of interest. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON], [PERSON]-W.; [PERSON], [PERSON]-A. Patch-based output space adversarial learning for joint optic disc and cup segmentation. _IEEE Trans. Med. Imaging_**2019**, _38_, 2485-2495. [CrossRef]
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