针对全卷积神经网络在图像分割中信息遗失、依赖固定权重导致分割精度低的问题,对U-Net结构进行改进并用于脑肿瘤磁共振(MR)图像的分割。在U-Net收缩路径上用注意力模块,将权重分布到不同尺寸的卷积层,有助于图像空间信息和上下文信息的利用;用残差紧密模块代替原有卷积层,能够提取更多的特征并促进网络收敛。基于BraTS(The Brain Tumor Image Segmentation Challenge)提供的脑肿瘤MR图像数据库,对提出的新模型进行验证,用Dice分数评估分割效果,获得肿瘤整体区域0.9056分、肿瘤核心区域0.7982分和肿瘤增强区域0.7861分的精度。由此表明本文提出的U-Net结构可提高MR图像分割的精度和效率。

Herein, U-Net structure was improved to segment magnetic resonance (MR) images of brain tumors to address the loss of information in image segmentation in the full convolutional neural network and low segmentation accuracy caused by fixed weights. Based on the attention module in the U-Net contraction path, the weights were distributed to different size convolutional layers, which is beneficial to information usage for image space and context. Replacing the original convolution layer with the residual compact module can extract more features and promote network convergence. The brain tumor MR image database provided by BraTS (The Brain Tumor Image Segmentation Challenge) is used to validate the proposed new model and evaluate the segmentation effect using the Dice score. The accuracy of 0.9056, 0.7982, and 0.7861 was obtained in the total tumor region, core tumor region, and tumor enhancement, respectively, demonstrating that the proposed U-Net structure can enhance the accuracy and efficiency of MR image segmentation.