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结合边缘特征提取的多模态脑部肿瘤图像分割网络

Multimodal Brain Tumor Image Segmentation Network Incorporating Edge Feature Extraction

  • 摘要: 多模态医学图像具有综合信息, 提高诊断准确性、检测疗效和预后评估等优势, 可以为医生提供更加全面、准确的信息. 为了利用多模态医学图像之间的综合信息, 提出结合边缘特征提取的多模态脑部肿瘤图像分割网络. 首先对预激活残差块进行改进, 提出双残差预激活模块替换U-Net中的编码与解码层, 并在解码层增加网络宽度, 加强了网络的特征提取能力; 然后设计一种边缘特征提取模块对不同模态和不同层次的边缘特征进行提取, 通过与分割结果进行融合强化网络对边缘病灶的表达能力. 在BraTS2018, BraTS2022数据集上进行实验的结果表明, 所提网络在全肿瘤区、肿瘤核心区和增强肿瘤区的相似系数Dice分别达到89.80%, 90.40%和82.60%, 以及91.73%, 93.06%和87.00%; 对于边界不太清晰的病灶区域, 该网络具有较高的精度和相对较低的冗余度, 总体表现优于现有的大部分网络.

     

    Abstract: Multimodal medical images have advantages such as comprehensive information, improving diagnostic accuracy, detecting therapeutic effects, and prognostic evaluation, which can provide doctors with more comprehensive and accurate information. This paper proposes a multimodal brain tumor image segmentation network that combines edge feature extraction to utilize the comprehensive information between multimodal medical images. The main design idea of the network is as follows: Firstly, the pre activation residual block has been improved, and a dual residual pre activation module has been proposed to replace the encoding and decoding layers in U-Net. Additionally, the network width has been increased in the decoding layer, enhancing the network's feature extraction ability; Then, an edge feature extraction module was designed to extract edge features from different modalities and levels, and integrate them with the segmentation results, enhancing the network's ability to express edge lesions; Finally, using the BraTS2018 and BraTS2022 datasets to validate network performance, the similarity coefficients Dice in the entire tumor area, tumor core area, and enhanced tumor area reached 89.80%, 90.40%, 82.60%, and 91.73%, 93.06%, and 87.00%, respectively. The proposed network has high accuracy and relatively low redundancy for lesion areas with unclear boundaries. Overall performance of the proposed network is better than most existing networks.

     

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