ARM-Net:基于注意机制和残差模块的改进型磁共振成像脑肿瘤分割方法

MingHu
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摘要

简介:准确的肿瘤分割是可靠诊断和治疗脑癌的先决条件。胶质瘤是一种高发且危及生命的脑肿瘤,由于脑部结构错综复杂,在脑部核磁共振成像图像上的表现难以预测,因此给分割带来了挑战:目前的脑肿瘤分割方法大多依赖于深度卷积神经网络,这种网络在编码和解码过程中会丢失大量特征信息,而且无法捕捉肿瘤轮廓的细节。它提出了一种基于注意机制和残差模块的改进型核磁共振成像脑肿瘤分割方法--ARM-Net。首先,在编码器的最后两层采用了倒置外部注意和扩张门控注意,使网络能够与病变区域和全局信息互动,从而促进四种模态之间更好的互动。其次,在编码器的前两层和解码器的后两层添加了不同数量的Res-Paths,以有效缓解传统跳转连接带来的语义间隙问题。结果:在BraTS 2019数据集上的实验表明,ARM-Net在分割性能上优于其他类似模型。结论:实验表明,ARM-Net模型能比其他方法更好地分割肿瘤的轮廓结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ARM-Net: Improved MRI brain tumor segmentation method based on attentional mechanism and residual module
INTRODUCTION: Accurate tumor segmentation is a prerequisite for reliable diagnosis and treatment of brain cancer. Gliomas, a highly prevalent and life-threatening type of brain tumor, pose a challenge for segmentation due to the intricate nature of brain structures and unpredictable appearances on brain MRI images.OBJECTIVES: Current methods for brain tumor segmentation mostly rely on deep convolutional neural networks, which suffer from significant loss of feature information during encoding and decoding and the inability to capture tumor contours in detail.METHODS: To address these challenges, this study rethinks the network architecture for MRI brain tumor segmentation. It proposes ARM-Net: an improved method for MRI brain tumor segmentation based on attention mechanisms and residual modules. Firstly, inverted external attention and dilated gated attention are employed in the last two layers of the encoder to enable the network to interact with both lesion areas and global information, facilitating better interaction among the four modalities. Secondly, different numbers of Res-Paths are added in the encoder's first two layers and the decoder's last two layers to effectively mitigate the semantic gap issues caused by traditional skip connections.RESULTS: Experiments on the BraTS 2019 dataset demonstrate that ARM-Net outperforms other similar models in terms of segmentation performance.CONCLUSION: The experiment showed that the ARM-Net model could segment the contour structure of the tumor better than other methods. 
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