Res-MulFra: Multilevel and Multiscale Framework for Brain Tumor Segmentation

Abstract

In clinical diagnosis and surgical planning, extracting brain tumors from magnetic resonance images (MRI) is very important. Nevertheless, considering the high variability and imbalance of the brain tumor datasets, the way of designing a deep neural network for accurately segmenting the brain tumor still challenges the researchers. Moreover, as the number of convolutional layers increases, the deep feature maps cannot provide fine-grained spatial information, and this feature information is useful for segmenting brain tumors from the MRI. Aiming to solve this problem, a brain tumor segmenting method of residual multilevel and multiscale framework (Res-MulFra) is proposed in this article. In the proposed framework, the multilevel is realized by stacking the proposed RMFM-based segmentation network (RMFMSegNet), which is mainly used to leverage the prior knowledge to gain a better brain tumor segmentation performance. The multiscale is implemented by the proposed RMFMSegNet, which includes both the parallel multibranch structure and the serial multibranch structure, and is mainly designed for obtaining the multiscale feature information. Moreover, from various receptive fields, a residual multiscale feature fusion module (RMFM) is also proposed to effectively combine the contextual feature information. Furthermore, in order to gain a better brain tumor segmentation performance, the channel attention module is also adopted. Through assessing the devised framework on the BraTS dataset and comparing it with other advanced methods, the effectiveness of the Res-MulFra is verified by the extensive experimental results. For the BraTS2015 testing dataset, the Dice value of the proposed method is 0.85 for the complete area, 0.72 for the core area, and 0.62 for the enhanced area.