Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associated with developmental and aging processes, existing brain tissue segmentation methods are only viable for specific age groups. Consequently, methods developed for one age group may fail for another. In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model. To overcome the challenges related to structural variability underpinned by biological processes, intensity inhomogeneity, motion artifacts, scanner-induced differences, and acquisition protocols, we propose to use contrastive learning to improve the quality of feature representations in a latent space for effective lifespan tissue segmentation. We compared our approach with commonly used segmentation methods on a large-scale dataset of 2,464 MR images. Experimental results show that our model accurately segments brain tissues across the lifespan and outperforms existing methods.
翻译:将大脑MR图像自动分割成白色物质(WM)、灰质(GM)和脑脊髓液(CSF)对于组织体积分析和皮质表面重建至关重要。由于与发育和老化过程有关的剧烈结构和外观变化,现有的脑组织分解方法只对特定年龄组可行。因此,为一个年龄组开发的方法可能因另一个年龄组而失败。在本文件中,我们首次尝试使用统一的深层次学习模型,将整个人类寿命(0-100岁)的脑组织分解成一个整体(0-100岁)的脑组织。为了克服与生物过程、强度不相容性、运动物件、扫描器引起的差异和获取协议所支撑的结构变异性有关的挑战,我们提议使用对比学习方法,提高潜在空间地貌表现的质量,以便有效的寿命组织分解。我们将我们的方法与2,464 MS图像的大规模数据集中常用的分解方法进行了比较。实验结果显示,我们的模型精确地显示,整个生命周期的分层脑组织以及超出现有方法。