CC-DA: Cross-Domain Consistency Data Augmentation for 3D Tumor Segmentation

Deep learning-based tumor segmentation in 3D medical images faces the challenges of limited annotated data and class imbalance. In this paper, we proposed a novel Cross-domain Consistency Data Augmentation (CC-DA) for 3D tumor segmentation. Specifically, we copy the tumor from source data and apply random transformations to enhance its diversity. Then, we paste the enhanced tumor into the organ area of target data to generate a new sample. This process can alleviate class imbalance by regulating the merged tumor pixel ratio. To further enhance the generated data credibility, we proposed a domain consistency constraint that aligns the source data distribution with the target data distribution. We conduct extensive experiments on KiTS19 and LiTS17 datasets. The promising results clearly show that our CC-DA method can effectively improve the existing state-of-the-art 3D tumor segmentation performance.