Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity

Abstract

The segmentation of glioma by computer vision is one of the hot topics in medical image analysis, which further helps doctors to make a better treatment plan for glioma. At present, convolutional neural networks (CNN) with multi-kernels are the mainstream method to identify glioma regions. However, the segmentation is strongly affected if the intensity dissimilarity between adjacent glioma regions is small. To solve this challenge, we propose an attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity to focus on the small-intensity dissimilarity glioma regions. Firstly, to reduce background interferences, this paper proposes data enhancement in glioma-centered regions. In addition, a random multi-dimensional data view is generated in the glioma regions to reduce overfitting. Secondly, we embed a 3D U-Net to proposed attention layers, which focus on the intensity dissimilarity between adjacent glioma regions, and adaptively mine the glioma-related features, solving the problem that the existing algorithms are insensitive to the small-intensity dissimilarity between adjacent glioma regions. In particular, each attention layer can adaptively highlight valuable glioma features and suppress unrelated ones. Finally, experiment results on the multimodal brain tumor segmentation challenge (BraTS) 2020 dataset validate the effectiveness of the proposed method, where the Dice Similarity Coefficients (DSC) are improved on the segmentation of whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions, reaching higher results at 0.7803, 0.8831 and 0.8172 for WT, TC and ET regions respectively. Also, we make a test on the public dataset BraTS2019, reaching the results at 0.7675, 0.8925, and 0.8110 for WT, TC, and ET regions, respectively.