Residual Block Based Nested U-Type Architecture for Multi-Modal Brain Tumor Image Segmentation

Sirui Chen, Shengjie Zhao, Quan Lan
2022 Frontiers in Neuroscience  
Multi-modal magnetic resonance imaging (MRI) segmentation of brain tumors is a hot topic in brain tumor processing research in recent years, which can make full use of the feature information of different modalities in MRI images, so that tumors can be segmented more effectively. In this article, convolutional neural networks (CNN) is used as a tool to improve the efficiency and effectiveness of segmentation. Based on this, Dense-ResUNet, a multi-modal MRI image segmentation model for brain
more » ... rs is created. The Dense-ResUNet consists of a series of nested dense convolutional blocks and a U-Net shaped model with residual connections. The nested dense convolutional blocks can bridge the semantic disparity between the feature maps of the encoder and decoder before fusion and make full use of different levels of features. The residual blocks and skip connection can extract pixel information from the image and skip the link to solve the traditional deep traditional CNN network problem. The experiment results show that our Dense-ResUNet can effectively help to extract the brain tumor and has great clinical research and application value.
doi:10.3389/fnins.2022.832824 pmid:35356052 pmcid:PMC8959850 fatcat:wcgz6kxgmfa6rkwozu5oimdr3u