Accurate segmentation of brain tumors has a vital impact on clinical diagnosis and treatment, and good segmentation results are helpful for the treatment of this disease, which is a serious threat to human health. High‐precision segmentation of brain tumors remains a challenging task due to their diverse shapes, sizes, locations, and complex boundaries. Considering the special structure of medical brain tumor images, many researchers have proposed a brain tumor segmentation (BraTS) network based on 3D U‐Net. However, there are also problems such as insufficient receptive fields and excessive computing costs. In this paper, we propose an efficient BraTS model based on group normalization (GN) and 3D U‐Net (3D‐EffUNet). First, according to the characteristics of brain tumor images, the medical image of the whole case is input into the model, and 3D convolution layers are used to extract features and filter irrelevant information. Then, using 3D U‐Net as the main framework, an efficient convolutional module is designed for more precise processing of brain tumor features. Moreover, an efficient convolution module based on GN and an attention mechanism is introduced to reduce the complexity of the network without affecting the segmentation performance and to increase the awareness of voxels between adjacent dimensions and the local space. Finally, the decoder was used to reconstruct high‐precision BraTS information. The model is trained and tested on the BraTS2021 dataset, and the experimental results show that it can maintain good segmentation performance and greatly reduce the calculation cost.

中文翻译：

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基于组归一化和 3D U-Net 的高效脑肿瘤分割模型

脑肿瘤的准确分割对临床诊断和治疗有着至关重要的影响，良好的分割结果有助于这种严重威胁人类健康的疾病的治疗。由于其不同的形状、大小、位置和复杂的边界，脑肿瘤的高精度分割仍然是一项具有挑战性的任务。考虑到医学脑肿瘤图像的特殊结构，许多研究人员提出了基于3D U-Net的脑肿瘤分割（BraTS）网络。但也存在感受野不足、计算成本过高等问题。在本文中，我们提出了一种基于组归一化（GN）和 3D U-Net（3D-EffUNet）的高效 BraTS 模型。首先，根据脑肿瘤图像的特点，将整个病例的医学图像输入到模型中，使用3D卷积层提取特征并过滤无关信息。然后，以3D U-Net为主要框架，设计了高效的卷积模块，以更精确地处理脑肿瘤特征。此外，引入了基于GN和注意力机制的高效卷积模块，在不影响分割性能的情况下降低网络复杂度，并增加对相邻维度和局部空间之间体素的感知。最后，使用解码器重建高精度BraTS信息。该模型在BraTS2021数据集上进行训练和测试，实验结果表明，该模型能够保持良好的分割性能，并大大降低计算成本。