Deformation Tumor Synthesis with Modal-Data Adaptive Supervision for 3D Brain Tumor Segmentation

The accurate segmentation of brain tumor is important not only for treatment planning, but also for follow-up evaluations. However, the inadequacy of annotated medical images poses challenges in training the brain tumor segmentation models. This paper addresses this issue by presenting a new method called De-formation Tumor Synthesis with Model-Data Adaptive Supervision DSMA . DSMA consists of data synthesis and weight allocation. The Deformation Tumor Synthesis DTSS strategy combines the morphological features of real tumors and adopts a unique iteration synthesis and fusion mechanism to generate diverse derived synthetic data customized for each set of real data. The Model-Data Adaptive Supervision MAS strategy dynamically filters and allocates the loss weights of synthetic data based on the real-time performance of the segmentation model to ensure the positive effects of adding synthetic data. The experimental results on the publicly available MRI brain imaging datasets BraTS2019 and BraTS2020 indicate that the proposed method achieves high-quality data synthesis and effectively improves the performance of the segmentation model.