准确的脑肿瘤分割在MRI的诊断和治疗监测中扮演着重要角色。然而，每个患者脑肿瘤区域的病变程度通常是不一致的，有大的结构差异，而脑肿瘤MR图像还具有低对比度和模糊的特点，目前深度学习算法通常无法实现准确的分割。为了解决这个问题，我们提出了一种新的端到端的脑肿瘤分割算法，将改进的3D U-Net网络和超分辨率图像重建集成到一个框架中。此外，在骨干网络的上采样操作之前嵌入坐标注意力模块，增强了局部纹理特征信息和全局位置特征信息的捕获能力。为了展示我们提出的算法在不同脑肿瘤MR图像中的分割结果，我们在BraTS数据集上进行了训练和评估，并通过dice相似度评分与其他深度学习算法进行了比较。在BraTS2021数据集上，我们的算法在增强型肿瘤、肿瘤核心和全肿瘤的dice相似度得分分别为89.61％、88.30％、91.05％，且Hausdorff距离分别为1.414毫米、7.810毫米和4.583毫米（95%）。实验结果说明，我们的方法优于基线3D U-Net方法，并在不同的数据集上表现出良好的性能，表明它对于具有明显差异的脑肿瘤MR图像的分割是具有鲁棒性的。Copyright © 2023 Elsevier Ltd. All rights reserved.

Accurate segmentation of brain tumor plays an important role in MRI diagnosis and treatment monitoring of brain tumor. However, the degree of lesions in each patient's brain tumor region is usually inconsistent, with large structural differences, and brain tumor MR images are characterized by low contrast and blur, current deep learning algorithms often cannot achieve accurate segmentation. To address this problem, we propose a novel end-to-end brain tumor segmentation algorithm by integrating the improved 3D U-Net network and super-resolution image reconstruction into one framework. In addition, the coordinate attention module is embedded before the upsampling operation of the backbone network, which enhances the capture ability of local texture feature information and global location feature information. To demonstrate the segmentation results of the proposed algorithm in different brain tumor MR images, we have trained and evaluated the proposed algorithm on BraTS datasets, and compared with other deep learning algorithms by dice similarity scores. On the BraTS2021 dataset, the proposed algorithm achieves the dice similarity score of 89.61%, 88.30%, 91.05%, and the Hausdorff distance (95%) of 1.414 mm, 7.810 mm, 4.583 mm for the enhancing tumors, tumor cores and whole tumors, respectively. The experimental results illuminate that our method outperforms the baseline 3D U-Net method and yields good performance on different datasets. It indicated that it is robust to segmentation of brain tumor MR images with structures vary considerably.Copyright © 2023 Elsevier Ltd. All rights reserved.