Accurate segmentation is a necessary step in the clinical management of brain tumors. However, the task remains challenging due to not only large variations in the sizes and shapes of brain tumors but also wide variations among individuals. In this paper, we develop a novel fully convolutional neural network with a feature reuse module and feature conformity module (F${}^2$ FCN) to alleviate the above challenges and further improve the accuracy of segmentation. Specifically, to extract more valuable features, we present a feature reuse module to repeatedly utilize features from different layers. We also provide a feature conformity module to eliminate possible noise and enhance the fusion of different feature map levels. However, the difficult selection of multiple parameters and the long training time of a single model make CNNs less effective. To solve these problems, a new distributed and parallel computing model, a hypergraph membrane system, is designed to implement the F${}^2$ FCN. In particular, we develop a hypergraph membrane structure with three new kinds of rules to implement several F${}^2$ FCNs with different settings simultaneously to leverage the ensemble learning of F${}^2$ FCNs and save time. Experimental results on two datasets show promotive performance in terms of the Dice similarity coefficient (DSC), positive predictive value (PPV) and sensitivity compared with the state-of-the-art methods.

准确分割是脑肿瘤临床治疗中的必要步骤。然而，该任务仍然具有挑战性，这不仅是由于脑肿瘤的大小和形状的巨大差异，而且由于个体之间的差异也很大。在本文中，我们开发了一种具有特征重用模块和特征整合模块（F的新颖的全卷积神经网络${}^2$FCN）以缓解上述挑战，并进一步提高细分的准确性。具体来说，为了提取更多有价值的特征，我们提出了一种特征重用模块，以重复利用来自不同层的特征。我们还提供了一个特征整合模块，以消除可能的噪音并增强不同特征图级别的融合。但是，多个参数的选择困难以及单个模型的训练时间长，使得CNN的效果较差。为了解决这些问题，设计了一种新的分布式并行计算模型，即超图膜系统，以实现F${}^2$FCN。特别是，我们使用三种新规则开发了超图膜结构，以实现多种F${}^2$ 具有不同设置的FCN同时使用F的整体学习${}^2$FCN并节省时间。在两个数据集上的实验结果表明，与最新技术方法相比，骰子相似性系数（DSC），正预测值（PPV）和敏感性具有促进作用。