Improving Single-Source Domain Generalization via Anatomy-Guided Texture Augmentation for Cervical Tumor Segmentation

Lixue Qin, Zhibo Xiao, Nazar Zaki, Yaoqin Xie, Wenjian Qin

Abstract: Single-Source domain generalization in medical image segmentation has been studied as a more practical configuration to solve domain shift issues in clinical applications. Data augmentation plays an important role in improving the diversity of training data. Recent data augmentation methods aim to randomize or disrupt the texture of images to encourage models to focus more on shape features, which are considered domain-invariant. It’s worth noting that texture features such as intensity variations are crucial cues for distinguishing the boundaries between the tumor and normal tissues. However, these features are often disrupted or compromised in existing methods. To effectively leverage these texture features and enhance the performance of the model, we propose a novel anatomy-guided texture augmentation (AGTA) method. Specifically, as imaging parameters vary, different organs or tissues may exhibit varying changes in intensity, while the intensity variations within each organ or tissue tend to remain consistent. To simulate this, we partition different organs into distinct regions based on the anatomical information of the image. Each region is then assigned random variations. We evaluated our method against other SDG methods in cross-modality and cross-center cervical tumor segmentation experiments. Our results show that our method outperforms all competing methods by a large margin.

External IDs:dblp:conf/cmmca/QinXZXQ24