Brain tumor segmentation using attention-based network in 3D MRI images

Abstract

Gliomas are the most common primary brain malignancies. Identifying the sub-regions of gliomas before surgery is meaningful, which may extend the survival of patients. However, due to the heterogeneous appearance and shape of gliomas, it is a challenge to accurately segment the enhancing tumor, the necrotic, the non-enhancing tumor core and the peritumoral edema. In this study, an attention-based network was used to segment the glioma sub-regions in multi-modality MRI scans. Attention U-Net was employed as the basic architecture of the proposed network. The attention gates help the network focus on the task-relevant regions in the image. Besides the spatial-wise attention gates, the channel-wise attention gates proposed in SE Net were also embedded into the segmentation network. This attention mechanism in the feature dimension prompts the network to focus on the useful feature maps. Furthermore, in order to reduce false positives, a training strategy combined with a sampling strategy was proposed in our study. The segmentation performance of the proposed network was evaluated on the BraTS 2019 validation dataset and testing dataset. In the validation dataset, the dice similarity coefficients of enhancing tumor, tumor core and whole tumor were 0.759, 0.807 and 0.893 respectively. And in the testing dataset, the dice scores of enhancing tumor, tumor core and whole tumor were 0.794, 0.814 and 0.866 respectively.