BACKGROUND:Brain tumor segmentation plays an important role in assisting diagnosis of disease, treatment plan planning, and surgical navigation. OBJECTIVE:This study aims to improve the accuracy of tumor boundary segmentation using the multi-scale U-Net network. METHODS:In this study, a novel U-Netwith dilated convolution (DCU-Net) structure is proposed for brain tumor segmentation based on the classic U-Net structure. First, the MR brain tumor images are pre-processed to alleviate the class imbalance problem by reducing the input of the background pixels. Then, the multi-scale spatial pyramid pooling is used to replace the max pooling at the end of the down-sampling path. It can expand the feature receptive field while maintaining image resolution. Finally, a dilated convolution residual block is combined to improve the skip connections in the training networks to improve the network’s ability to recognize the tumor details. RESULTS:The proposed model has been evaluated using the Brain Tumor Segmentation (BRATS) 2018 Challenge training dataset and achieved the dice similarity coefficients (DSC) score of 0.91, 0.78 and 0.83 for whole tumor, core tumor and enhancing tumor segmentation, respectively. CONCLUSIONS:The experiment results indicate that the proposed model yields a promising performance in automated brain tumor segmentation.

中文翻译：

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DCU-Net：用于脑肿瘤分割的多尺度 U-Net。

背景：脑肿瘤分割在辅助疾病诊断、治疗计划制定和手术导航方面发挥着重要作用。目的：本研究旨在利用多尺度U-Net网络提高肿瘤边界分割的准确性。方法：在本研究中，基于经典的 U-Net 结构，提出了一种具有扩张卷积（DCU-Net）结构的新型 U-Net 用于脑肿瘤分割。首先，对MR脑肿瘤图像进行预处理，通过减少背景像素的输入来缓解类不平衡问题。然后，多尺度空间金字塔池化用于替换下采样路径末端的最大池化。它可以在保持图像分辨率的同时扩大特征感受野。最后，结合扩张的卷积残差块来改进训练网络中的跳跃连接，从而提高网络识别肿瘤细节的能力。结果：已使用脑肿瘤分割 (BRATS) 2018 Challenge 训练数据集对所提出的模型进行了评估，并在整个肿瘤、核心肿瘤和增强肿瘤分割方面的骰子相似系数 (DSC) 得分分别为 0.91、0.78 和 0.83。结论：实验结果表明，所提出的模型在自动脑肿瘤分割中产生了有希望的性能。整个肿瘤、核心肿瘤和增强肿瘤分割分别为 78 和 0.83。结论：实验结果表明，所提出的模型在自动脑肿瘤分割中产生了有希望的性能。整个肿瘤、核心肿瘤和增强肿瘤分割分别为 78 和 0.83。结论：实验结果表明，所提出的模型在自动脑肿瘤分割中产生了有希望的性能。