Chen Sirui, Zhao Shengjie, Lan Quan
School of Software Engineering, Tongji University, Shanghai, China.
Department of Neurology, First Affiliated Hospital of Xiamen University, Xiamen, China.
Front Neurosci. 2022 Mar 9;16:832824. doi: 10.3389/fnins.2022.832824. eCollection 2022.
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 tumors 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.
脑肿瘤的多模态磁共振成像（MRI）分割是近年来脑肿瘤处理研究中的一个热门话题，它可以充分利用MRI图像中不同模态的特征信息，从而更有效地分割肿瘤。在本文中，卷积神经网络（CNN）被用作提高分割效率和效果的工具。基于此，创建了一种用于脑肿瘤的多模态MRI图像分割模型Dense-ResUNet。Dense-ResUNet由一系列嵌套的密集卷积块和一个带有残差连接的U-Net形状模型组成。嵌套的密集卷积块可以在融合前弥合编码器和解码器特征图之间的语义差异，并充分利用不同层次的特征。残差块和跳跃连接可以从图像中提取像素信息并通过跳跃连接解决传统深度CNN网络的问题。实验结果表明，我们的Dense-ResUNet能够有效地帮助提取脑肿瘤，具有很大的临床研究和应用价值。
