论文标题
图神经网络和基于超像素的脑组织分割（校正版本）
Graph Neural Network and Superpixel Based Brain Tissue Segmentation (Corrected Version)
论文作者
论文摘要
卷积神经网络（CNN）通常用作设计生物医学图像分割方法的骨干。但是，接受场和大量参数的局限性限制了这些方法的性能。在本文中，我们提出了一种基于图形神经网络（GNN）的方法，名为GNN-SEG，用于分割脑组织。与常规CNN的方法不同，GNN-SEG将Superpixels作为基本处理单元，并使用GNN来学习脑组织的结构。此外，受生物视觉系统中的相互作用机制的启发，我们提出了两种相互作用模块，以增强功能和集成。在实验中，我们将GNN-SEG与脑磁共振图像的四个数据集上的基于最新的CNN方法进行了比较。实验结果表明GNN-SEG的优越性。
Convolutional neural networks (CNNs) are usually used as a backbone to design methods in biomedical image segmentation. However, the limitation of receptive field and large number of parameters limit the performance of these methods. In this paper, we propose a graph neural network (GNN) based method named GNN-SEG for the segmentation of brain tissues. Different to conventional CNN based methods, GNN-SEG takes superpixels as basic processing units and uses GNNs to learn the structure of brain tissues. Besides, inspired by the interaction mechanism in biological vision systems, we propose two kinds of interaction modules for feature enhancement and integration. In the experiments, we compared GNN-SEG with state-of-the-art CNN based methods on four datasets of brain magnetic resonance images. The experimental results show the superiority of GNN-SEG.
