This paper presents a novel unsupervised algorithm for brain tissue segmentation in magnetic resonance imaging (MRI). The proposed algorithm, named Gardens2, adopts a clustering approach to segment voxels of a given MRI into three classes: cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM). Using an overlapping criterion, 3D feature descriptors and prior atlas information, Gardens2 generates a segmentation mask per class in order to parcellate the brain tissues. We assessed our method using three neuroimaging datasets: BrainWeb, IBSR18, and IBSR20, the last two provided by the Internet Brain Segmentation Repository. Its performance was compared with eleven well established as well as newly proposed unsupervised segmentation methods. Overall, Gardens2 obtained better segmentation performance than the rest of the methods in two of the three databases and competitive results when its performance was measured by class.

本文提出了一种用于磁共振成像 (MRI) 中脑组织分割的新型无监督算法。所提出的算法名为 Gardens2，采用聚类方法将给定 MRI 的体素分为三类：脑脊液 (CSF)、灰质 (GM) 和白质 (WM)。使用重叠标准、3D 特征描述符和先前的图集信息，Gardens2 为每个类生成一个分割掩码，以分割脑组织。我们使用三个神经影像数据集评估了我们的方法：BrainWeb、IBSR18 和 IBSR20，最后两个由 Internet Brain Segmentation Repository 提供。它的性能与十一种成熟的以及新提出的无监督分割方法进行了比较。全面的，