A novel M-SegNet with global attention CNN architecture for automatic segmentation of brain MRI

In this paper, we propose a novel M-SegNet architecture with global attention for the segmentation of brain magnetic resonance imaging (MRI). The proposed architecture consists of a multiscale deep network at the encoder side, deep supervision at the decoder side, a global attention mechanism, different sizes of convolutional kernels, and combined-connections with skip connections and pooling indices. The multiscale side input layers were used to support deep layers for extracting the discriminative information and the upsampling layer at the decoder side provided deep supervision, which reduced the gradient problem. The global attention mechanism is utilized to capture rich contextual information in the decoder stage by integrating local features with their respective global dependencies. In addition, multiscale convolutional kernels of different sizes were used to extract abundant semantic features from brain MRI scans in the encoder and decoder modules. Moreover, combined-connections were used to pass features from the encoder to the decoder path to recover the spatial information lost during downsampling and makes the model converge faster. Furthermore, we adopted uniform non-overlapping input patches to focus on fine details for the segmentation of brain MRI. We verified the proposed architecture on publicly accessible datasets for the task of segmentation of brain MRI. The experimental results show that the proposed model outperforms conventional methods by achieving an average Dice similarity coefficient score of 0.96. • The key contribution of the paper is to propose a novel M-SegNet with global attention architecture for segmentation of brain on MRI. The global attention provides a global context as a reference for low-level features for the selection of category localization information. • The proposed model uses combined-connections to pass features from the encoder to the decoder blocks to recover the spatial information and makes the model converge faster. • To increase the segmentation accuracy of brain MRI, we used different sizes of convolutional kernels to extract abundant semantic features at the encoder and decoder blocks.