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Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities
Neurocomputing ( IF 5.5 ) Pub Date : 2021-09-20 , DOI: 10.1016/j.neucom.2021.09.032
Tongxue Zhou 1, 2, 3 , Stéphane Canu 1, 3 , Pierre Vera 2, 4 , Su Ruan 2, 3
Affiliation  

Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate brain tumor segmentation. The main problem is that not all types of MRIs are always available in clinical exams. Based on the fact that there is a strong correlation between MR modalities of the same patient, in this work, we propose a novel brain tumor segmentation network in the case of missing one or more modalities. The proposed network consists of three sub-networks: a feature-enhanced generator, a correlation constraint block and a segmentation network. The feature-enhanced generator utilizes the available modalities to generate 3D feature-enhanced image representing the missing modality. The correlation constraint block can exploit the multi-source correlation between the modalities and also constrain the generator to synthesize a feature-enhanced modality which must have a coherent correlation with the available modalities. The segmentation network is a multi-encoder based U-Net to achieve the final brain tumor segmentation. The proposed method is evaluated on BraTS 2018 dataset. Experimental results demonstrate the effectiveness of the proposed method which achieves the average Dice Score of 82.9, 74.9 and 59.1 on whole tumor, tumor core and enhancing tumor, respectively across all the situations, and outperforms the best method by 3.5%, 17% and 18.2%.



中文翻译:

基于特征增强生成和多模态融合的深度神经网络,用于缺失 MR 模态的脑肿瘤分割

使用多模态磁共振成像 (MRI) 是准确分割脑肿瘤所必需的。主要问题是,并非所有类型的 MRI 都可用于临床检查。基于同一患者的 MR 模式之间存在强相关性这一事实,在这项工作中,我们提出了一种新的脑肿瘤分割网络,用于在缺少一种或多种模式的情况下。所提出的网络由三个子网络组成:一个特征增强生成器、一个相关约束块和一个分割网络。特征增强生成器利用可用的模态来生成表示缺失模态的 3D 特征增强图像。相关约束块可以利用模态之间的多源相关性,还可以约束生成器合成一个特征增强的模态,该模态必须与可用的模态具有相干相关性。分割网络是一个基于多编码器的 U-Net 来实现最终的脑肿瘤分割。所提出的方法在 BraTS 2018 数据集上进行了评估。实验结果证明了所提出的方法的有效性,在所有情况下,在整个肿瘤、肿瘤核心和增强肿瘤上的平均 Dice Score 分别达到 82.9、74.9 和 59.1,并且优于最佳方法 3.5%、17% 和 18.2 %。所提出的方法在 BraTS 2018 数据集上进行了评估。实验结果证明了所提出的方法的有效性,在所有情况下,在整个肿瘤、肿瘤核心和增强肿瘤上的平均 Dice Score 分别达到 82.9、74.9 和 59.1,并且优于最佳方法 3.5%、17% 和 18.2 %。所提出的方法在 BraTS 2018 数据集上进行了评估。实验结果证明了所提出方法的有效性,该方法在所有情况下对整个肿瘤、肿瘤核心和增强肿瘤的平均 Dice Score 分别达到 82.9、74.9 和 59.1,并且优于最佳方法 3.5%、17% 和 18.2 %。

更新日期:2021-10-01
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