Performance degradation due to covariate shift remains a major challenge for deep learning models in medical image segmentation. An open question is whether samples from a shifted distribution can effectively support learning when combined with limited target domain data. We investigate this problem in the context of bladder segmentation in CT guided gynecological brachytherapy, a critical task for accurate dose optimization and organ at risk sparing. While CT scans without brachytherapy applicators (no applicator: NA) are widely available, scans with applicators inserted (with applicator: WA) are scarce and exhibit substantial anatomical deformation and imaging artifacts, making automated segmentation particularly difficult. We propose a dual domain learning strategy that integrates NA and WA CT data to improve robustness and generalizability under covariate shift. Using a curated assorted dataset, we show that NA data alone fail to capture the anatomical and artifact related characteristics of WA images. However, introducing a modest proportion of WA data into a predominantly NA training set leads to significant performance improvements. Through systematic experiments across axial, coronal, and sagittal planes using multiple deep learning architectures, we demonstrate that doping only 10 to 30 percent WA data achieves segmentation performance comparable to models trained exclusively on WA data. The proposed approach attains Dice similarity coefficients of up to 0.94 and Intersection over Union scores of up to 0.92, indicating effective domain adaptation and improved clinical reliability. This study highlights the value of integrating anatomically similar but distribution shifted datasets to overcome data scarcity and enhance deep learning based segmentation for brachytherapy treatment planning.

翻译：协变量偏移导致的性能下降仍是医学图像分割中深度学习模型面临的主要挑战。一个悬而未决的问题是：当与有限的目标域数据结合时，来自偏移分布的样本能否有效支持学习？我们在CT引导妇科近距离放疗的膀胱分割任务中研究此问题，该任务对于精确剂量优化和危及器官保护至关重要。虽然不含近距离放疗施源器的CT扫描图像（无施源器：NA）广泛可得，但插入施源器后的扫描图像（有施源器：WA）却十分稀缺，且存在显著的解剖结构变形和成像伪影，使得自动分割尤为困难。我们提出一种双域学习策略，通过整合NA与WA CT数据来提升模型在协变量偏移下的鲁棒性和泛化能力。基于精心构建的混合数据集，我们发现仅使用NA数据无法捕捉WA图像中与解剖结构及伪影相关的特征。然而，在主要由NA数据组成的训练集中引入适量比例的WA数据，可带来显著的性能提升。通过使用多种深度学习架构在轴状面、冠状面和矢状面进行系统实验，我们证明仅掺杂10%至30%的WA数据即可达到与完全使用WA数据训练模型相当的分割性能。所提方法获得的Dice相似系数最高达0.94，交并比最高达0.92，表明其实现了有效的域适应并提升了临床可靠性。本研究凸显了整合解剖结构相似但分布偏移的数据集对于克服数据稀缺性、增强基于深度学习的近距离放疗计划分割的重要价值。