A Squeeze U-SegNet Architecture Based on Residual Convolution for Brain MRI Segmentation

Abstract

This paper proposes an improved brain magnetic resonance imaging (MRI) segmentation model by integrating U-SegNet with fire modules and residual convolutions to segment brain tissues in MRI. In the proposed encoder-decoder method, the residual connections and squeeze-expand convolutional layers from the fire module lead to a lighter and more efficient architecture for brain MRI segmentation. The residual unit helps in the smooth training of the deep architecture, and features obtained from residual convolutions exhibit a superior representation of the features in the segmentation network. In addition, the method provides a design with more efficient architecture, fewer network parameters, and better segmentation accuracy for brain MRI. The proposed architecture was evaluated on publicly available open access series of imaging studies (OASIS) and internet brain segmentation repository (IBSR) datasets for brain tissue segmentation. The experimental results showed superior performance compared to other state-of-the-art methods on brain MRI segmentation with a dice similarity coefficient (DSC) score of 0.96 and Jaccard index (JI) of 0.92.