Matpr-Unet: A Multi Attention Two-Path Residual Unet for Focal Cortical Dysplasia Lesions Segmentation

Medical imaging is now a widely used test for the preoperative evaluation of focal cortical dysplasia (FCD). Deep learning-based methods can learn lesion features from image data to automatically recognize and segment FCD in epilepsy treatment. However, the existing FCD segmentation networks lack the ability to fully extract the FCD lesion information and automatically aggregate the salient features of the lesions, the segmentation accuracy needs to be improved. To this end, we propose an end-to-end 3D Convolutional Neural Network segmentation model, Multi Attention Two-Path Residual UNet (MATPR-UNet). Specifically, we propose two modules: (1) Two-Path Residual Attention module, which can extract both local and global information, and suppress invalid information by fully fusing the features in channel and space; (2) Spatially Gated Attention module, which enables the model to automatically focus on the FCD lesion region, highlighting its salient features. We combine the proposed two modules with the 3D UNet to construct the MATPR-UNet. Extensive experiments on the private FCD dataset and the public EPISURG dataset demonstrate that our method outperforms other state-of-the-art methods, and is robust.