Brain tumor segmentation on multimodal MR imaging using multi-level upsampling in decoder

Abstract

Accurate brain tumor segmentation plays a pivotal role in clinical practice and research settings. In this paper, we propose the multi-level up-sampling network (MU-Net) to learn the image presentations of transverse, sagittal and coronal view and fuse them to automatically segment brain tumors, including necrosis, edema, non-enhancing, and enhancing tumor, in multimodal magnetic resonance (MR) sequences. The MU-Net model has an encoder–decoder structure, in which low level feature maps obtained by the encoder and high level feature maps obtained by the decoder are combined by using a newly designed global attention (GA) module. The proposed model has been evaluated on the BraTS 2018 Challenge validation dataset and achieved an average Dice similarity coefficient of 0.88, 0.74, 0.69 and 0.85, 0.72, 0.66 for the whole tumor, core tumor and enhancing tumor on the validation dataset and testing dataset, respectively. Our results indicate that the proposed model has a promising performance in automated brain tumor segmentation.