Brain tumor image segmentation based on shuffle transformer-dynamic convolution and inception dilated convolution

Abstract

Accurate segmentation of brain tumors is essential for accurate clinical diagnosis and effective treatment. Convolutional neural networks (CNNs) have improved brain tumor segmentation with their excellent performance in local feature modeling. However, they still face the challenge of unpredictable changes in tumor size and location, because it cannot be effectively matched by CNN-based methods with local and regular receptive fields. To overcome these obstacles, we propose brain tumor image segmentation based on shuffle transformer-dynamic convolution and inception dilated convolution that captures and adapts different features of tumors through multi-scale feature extraction. Our model combines Shuffle Transformer-Dynamic Convolution (STDC) to capture both fine-grained and contextual image details so that it helps improve localization accuracy. In addition, the Inception Dilated Convolution(IDConv) module solves the problem of significant changes in the size of brain tumors, and then captures the information of different size of object. The multi-scale feature aggregation(MSFA) module integrates features from different encoder levels, which contributes to enriching the scale diversity of input patches and enhancing the robustness of segmentation. The experimental results conducted on the BraTS 2019, BraTS 2020, BraTS 2021, and MSD BTS datasets indicate that our model outperforms other state-of-the-art methods in terms of accuracy.