Mayala Simeon, Herdlevær Ida, Haugsøen Jonas Bull, Anandan Shamundeeswari, Blaser Nello, Gavasso Sonia, Brun Morten
Department of Mathematics, University of Bergen, 5020 Bergen, Norway.
Department of Clinical Medicine, University of Bergen, 5021 Bergen, Norway.
J Imaging. 2022 Sep 27;8(10):262. doi: 10.3390/jimaging8100262.
Brain segmentation in magnetic resonance imaging (MRI) images is the process of isolating the brain from non-brain tissues to simplify the further analysis, such as detecting pathology or calculating volumes. This paper proposes a Graph-based Unsupervised Brain Segmentation (GUBS) that processes 3D MRI images and segments them into brain, non-brain tissues, and backgrounds. GUBS first constructs an adjacency graph from a preprocessed MRI image, weights it by the difference between voxel intensities, and computes its minimum spanning tree (MST). It then uses domain knowledge about the different regions of MRIs to sample representative points from the brain, non-brain, and background regions of the MRI image. The adjacency graph nodes corresponding to sampled points in each region are identified and used as the terminal nodes for paths connecting the regions in the MST. GUBS then computes a subgraph of the MST by first removing the longest edge of the path connecting the terminal nodes in the brain and other regions, followed by removing the longest edge of the path connecting non-brain and background regions. This process results in three labeled, connected components, whose labels are used to segment the brain, non-brain tissues, and the background. GUBS was tested by segmenting 3D T1 weighted MRI images from three publicly available data sets. GUBS shows comparable results to the state-of-the-art methods in terms of performance. However, many competing methods rely on having labeled data available for training. Labeling is a time-intensive and costly process, and a big advantage of GUBS is that it does not require labels.
磁共振成像（MRI）图像中的脑部分割是将大脑与非脑组织分离的过程，以简化进一步的分析，例如检测病变或计算体积。本文提出了一种基于图的无监督脑部分割（GUBS）方法，该方法用于处理三维MRI图像，并将其分割为脑、非脑组织和背景。GUBS首先从预处理后的MRI图像构建邻接图，根据体素强度之间的差异对其加权，然后计算其最小生成树（MST）。然后，它利用关于MRI不同区域的领域知识，从MRI图像的脑、非脑和背景区域中采样代表性点。识别每个区域中与采样点对应的邻接图节点，并将其用作连接MST中各区域路径的终端节点。GUBS然后通过首先移除连接脑区和其他区域终端节点的路径中最长的边，接着移除连接非脑区和背景区域路径中最长的边，来计算MST的子图。这个过程产生三个标记的连通分量，其标签用于分割脑、非脑组织和背景。通过对来自三个公开可用数据集的三维T1加权MRI图像进行分割，对GUBS进行了测试。在性能方面，GUBS显示出与现有最先进方法相当的结果。然而，许多竞争方法依赖于有标记数据用于训练。标记是一个耗时且成本高昂的过程，而GUBS的一个很大优势是它不需要标记。
