Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation

Neuronal cell bodies mostly reside in the cerebral cortex. The study of this thin and highly convoluted surface is essential for understanding how the brain works. The analysis of surface data is, however, challenging due to the high variability of the cortical geometry. This paper presents a novel approach for learning and exploiting surface data directly across multiple surface domains. Current approaches rely on geometrical simplifications, such as spherical inflations, a popular but costly process. For instance, the widely used FreeSurfer takes about 3 hours to parcellate brain surfaces on a standard machine. Direct learning of surface data via graph convolutions would provide a new family of fast algorithms for processing brain surfaces. However, the current limitation of existing state-of-the-art approaches is their inability to compare surface data across different surface domains. Surface bases are indeed incompatible between brain geometries. This paper leverages recent advances in spectral graph matching to transfer surface data across aligned spectral domains. This novel approach enables direct learning of surface data across compatible surface bases. It exploits spectral filters over intrinsic representations of surface neighborhoods. We illustrate the benefits of this approach with an application to brain parcellation. We validate the algorithm over 101 manually labeled brain surfaces. The results show a significant improvement in labeling accuracy over recent Euclidean approaches while gaining a drastic speed improvement over conventional methods. (C) 2019 Elsevier B.V. All rights reserved.

神经元细胞体大多位于大脑皮层。对这个薄且高度褶皱的表面进行研究对于理解大脑如何工作至关重要。然而，由于皮层几何形状的高度可变性，表面数据的分析具有挑战性。本文提出了一种直接跨多个表面区域学习和利用表面数据的新方法。当前的方法依赖于几何简化，比如球形膨胀，这是一个流行但耗时的过程。例如，广泛使用的FreeSurfer在标准机器上对大脑表面进行分区大约需要3小时。通过图卷积直接学习表面数据将为处理大脑表面提供一系列新的快速算法。然而，现有最先进方法的当前局限性在于它们无法比较不同表面区域的表面数据。大脑几何形状之间的表面基确实不兼容。本文利用光谱图匹配的最新进展，在对齐的光谱区域之间传递表面数据。这种新方法能够在兼容的表面基上直接学习表面数据。它利用表面邻域的内在表示上的光谱滤波器。我们通过在大脑分区上的应用来说明这种方法的优势。我们在101个手动标记的大脑表面上验证了该算法。结果显示，与近期的欧几里得方法相比，标记准确性有显著提高，同时与传统方法相比，速度有极大提升。(C) 2019爱思唯尔有限公司。保留所有权利。