Brain tumour segmentation using U-net based adversarial networks

Abstract

The segmentation of medical images on brain tumour faces many challenges. For example, the brain image needs to be divided accurately into non-enhancing tumour, enhancing tumour, tumour core and undamaged area. This paper utilizes four state-of-the-art convolution architectures to perform the segmentation of brain tumour, including the generative adversarial networks (GANs), conditional deep convolution GANs, auto-encoders and u-nets. Based on adversarial networks, the author put forward a novel model for Image segmentation. The model consists of two parts: Auto encoders as generator and Convolution network as discriminator. The proposed model was applied to segment the brain tumour images proposed by Medical Image Computing and Computer-Assisted Interventions Conference (MICCAI), and evaluated by indices like mean accuracy, mean loss and mean intersection over union (IoU). The results show that our model outperformed the traditional algorithms in segmentation effects. The research findings reflect the effectiveness of GANs in segmentation tasks.