College of Electrical Engineering And Control Science, Nanjing Tech University NanJing, People's Republic of China.
Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University NanJing, People's Republic of China.
Phys Med Biol. 2023 Sep 26;68(19). doi: 10.1088/1361-6560/acf911.
. In brain tumor segmentation tasks, the convolutional neural network (CNN) or transformer is usually acted as the encoder since the encoder is necessary to be used. On one hand, the convolution operation of CNN has advantages of extracting local information although its performance of obtaining global expressions is bad. On the other hand, the attention mechanism of the transformer is good at establishing remote dependencies while it is lacking in the ability to extract high-precision local information. Either high precision local information or global contextual information is crucial in brain tumor segmentation tasks. The aim of this paper is to propose a brain tumor segmentation model that can simultaneously extract and fuse high-precision local and global contextual information.. We propose a network model DE-Uformer with dual encoders to obtain local features and global representations using both CNN encoder and Transformer encoder. On the basis of this, we further propose the nested encoder-aware feature fusion (NEaFF) module for effective deep fusion of the information under each dimension. It may establishe remote dependencies of features under a single encoder via the spatial attention Transformer. Meanwhile ,it also investigates how features extracted from two encoders are related with the cross-encoder attention transformer.. The proposed algorithm segmentation have been performed on BraTS2020 dataset and private meningioma dataset. Results show that it is significantly better than current state-of-the-art brain tumor segmentation methods.. The method proposed in this paper greatly improves the accuracy of brain tumor segmentation. This advancement helps healthcare professionals perform a more comprehensive analysis and assessment of brain tumors, thereby improving diagnostic accuracy and reliability. This fully automated brain model segmentation model with high accuracy is of great significance for critical decisions made by physicians in selecting treatment strategies and preoperative planning.
. 在脑肿瘤分割任务中，卷积神经网络（CNN）或转换器通常被用作编码器，因为需要使用编码器。一方面，CNN 的卷积操作具有提取局部信息的优势，尽管其获取全局表达的性能较差。另一方面，转换器的注意力机制善于建立远程依赖关系，而缺乏提取高精度局部信息的能力。在脑肿瘤分割任务中，无论是高精度的局部信息还是全局上下文信息都至关重要。本文的目的是提出一种能够同时提取和融合高精度局部和全局上下文信息的脑肿瘤分割模型。. 我们提出了一个具有双编码器的网络模型 DE-Uformer，使用 CNN 编码器和 Transformer 编码器来获取局部特征和全局表示。在此基础上，我们进一步提出了嵌套编码器感知特征融合（NEaFF）模块，用于有效融合每个维度下的信息。它可以通过空间注意力 Transformer 建立单一编码器下特征的远程依赖关系。同时，它还研究了从两个编码器提取的特征与交叉编码器注意力 Transformer 之间的关系。. 该算法已在 BraTS2020 数据集和私人脑膜瘤数据集上进行了分割。结果表明，它明显优于当前最先进的脑肿瘤分割方法。. 本文提出的方法极大地提高了脑肿瘤分割的准确性。这一进展有助于医疗保健专业人员对脑肿瘤进行更全面的分析和评估，从而提高诊断的准确性和可靠性。这种具有高精度的全自动脑模型分割模型对于医生在选择治疗策略和术前规划时做出的关键决策具有重要意义。
