Grande-Barreto Jonas, Gómez-Gil Pilar
National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico.
Med Biol Eng Comput. 2020 Dec;58(12):3101-3112. doi: 10.1007/s11517-020-02270-1. Epub 2020 Nov 5.
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. Graphical Abstract Brain tissue segmentation using 3D features and an adjusted atlas template.
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的体素分割为三类：脑脊液（CSF）、灰质（GM）和白质（WM）。利用重叠准则、3D特征描述符和先验图谱信息，Gardens2为每个类别生成一个分割掩码，以便对脑组织进行分区。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，后两个数据集由互联网脑图谱库提供。将其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。总体而言，Gardens2在三个数据库中的两个中获得了比其他方法更好的分割性能，并且按类别衡量其性能时得到了具有竞争力的结果。图形摘要 使用3D特征和调整后的图谱模板进行脑组织分割。
