This study proposes an improved U‐Net model to address the issues of large semantic differences in skip connections and insufficient utilization of cross‐channel information in magnetic resonance imaging (MRI) images leading to inaccurate segmentation of brain tumor regions in the field of brain tumor segmentation. Firstly, by adding a deep residual module to alter the receptive field, the network's ability to learn tumor information is enhanced. Secondly, a dual attention mechanism was established using Bayesian weighting technology, achieving multi‐channel and multi‐scale feature fusion, and improving the model's learning and extraction of brain tumor boundary information. Finally, the tumor features extracted from different patterns are concatenated through skip connections, effectively integrating feature information from different levels and scales, and reducing semantic differences. We evaluated the performance of the proposed model on the BraTS2018 and BraTS2019 brain tumor image segmentation datasets. The experimental results showed that for the BraTS2018 dataset, the model improved the average dice score by 12.8%, the average sensitivity by 10.4%, and the average Hausdorff Distance by 5.75 compared to traditional U‐Net. On the BraTS2019 dataset, three indicators improved by 12.6%, 11.2%, and 7.46, respectively. The experimental results show that the proposed improved U‐Net model can improve the segmentation performance of brain tumor MRI images without increasing computational time.

中文翻译：

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基于贝叶斯权重分布多模态特征融合的脑肿瘤图像分割算法

本研究提出一种改进的U-Net模型，以解决脑肿瘤领域中跳跃连接语义差异较大以及磁共振成像（MRI）图像跨通道信息利用不足导致脑肿瘤区域分割不准确的问题分割。首先，通过添加深度残差模块来改变感受野，增强网络学习肿瘤信息的能力。其次，利用贝叶斯加权技术建立双重注意力机制，实现多通道、多尺度特征融合，提高模型对脑肿瘤边界信息的学习和提取。最后，将不同模式提取的肿瘤特征通过跳跃连接连接起来，有效整合不同层次和尺度的特征信息，减少语义差异。我们评估了所提出的模型在 BraTS2018 和 BraTS2019 脑肿瘤图像分割数据集上的性能。实验结果表明，对于BraTS2018数据集，该模型与传统U-Net相比，平均dice得分提高了12.8%，平均灵敏度提高了10.4%，平均Hausdorff距离提高了5.75。在BraTS2019数据集上，三项指标分别提升了12.6%、11.2%和7.46。实验结果表明，所提出的改进的U-Net模型可以在不增加计算时间的情况下提高脑肿瘤MRI图像的分割性能。