FMNet: feature mining networks for brain tumor segmentation

The brain tumor is one of the primary diseases that endanger human life and health. Multi-modality magnetic resonance imaging (MRI) for tumor analysis is one of the key techniques for clinical diagnosis. Experienced experts artificially segment the classical method of brain tumor segmentation according to their anatomical and pathological knowledge, which is time-consuming. In this paper, we propose a novel deep neural network structure, namely Feature Mining Networks (FMNet), for brain tumor segmentation. The proposed FMNet adopts three innovative structure, including semantic information mining unit (SIMU), macro information mining unit (MIMU), and a feature correction unit (FCU). These three units can enhance the mining of semantic information and spatial information, and further, modify the information in a direction that is conducive to segmentation results. Each unit can bring significant improvement in segmentation performance. We evaluate the proposed framework on BraTS2017 and BraTS2018 dataset. The experimental results show that our FMNet performs better than state-of-the-art networks such as fully convolutional networks (FCN), U-Net, VGG, and Hybrid Pyramid U-Net (HPUNet).

Recommended citation: Fengming Lin, et al. FMNet: feature mining networks for brain tumor segmentation. In 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 555-560). IEEE. http://fmlinks.github.io/files/paper_lin2019fmnet.pdf