A Deep Neural Network-Based Segmentation Method for Multimodal Brain Tumor Images

Medical image segmentation plays an important role in medical diagnosis. Accurate segmentations of brain tumor images require well-designed segmentation models and sufficient high-quality well-labeled training samples, but it is difficult for existing segmentation methods to meet these requirements. In this paper, we propose a segmentation method, which involves a GAN-nested model and an improved UNet. The GAN-nested model is used to automatically generate sufficient well-labeled brain tumor images, which are used as training samples; the improved UNet is good at extracting the detailed features of brain tumor images, and therefore it can conduct the accurate segmentation for brain tumor images using high-quality training samples generated by the GAN-nested model. Extensive experimental results prove that the proposed method is effective and obtains state-of-the-art performance on the given datasets.