ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Alberto Fernández-Pena, Daniel Martín de Blas, Francisco J Navas-Sánchez, Luis Marcos-Vidal, Pedro M Gordaliza, Javier Santonja, Joost Janssen, Susanna Carmona, Manuel Desco, Yasser Alemán-Gómez
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Abstract

The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns, which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to reliably segment sulcal fundi and gyral crown lines. ABLE is built to work on standard FreeSurfer outputs and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method for obtaining sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE .

Abstract Image

ABLE:基于拉普拉斯表面塌陷的自动脑线提取。
人类皮层的典型折叠形状一直是神经科学研究的一个长期课题。然而,准确的神经解剖学分割沟仍然是一个挑战。这个问题的部分原因是不确定脑沟和脑回在哪里过渡,反之亦然。这个问题可以通过关注沟底和回冠来避免,它们代表皮层折叠的拓扑相反。我们提出了一种基于拉普拉斯表面塌陷的自动脑线提取(ABLE)方法,以可靠地分割沟底和脑回冠线。ABLE是建立在标准的FreeSurfer输出上,避免了吻合沟的划定,同时保持吻合沟底线穿过具有最高深度和曲率的区域。首先,它将皮层分割成脑回和脑沟表面;然后,对每个表面进行空间滤波。采用基于拉普拉斯坍缩的算法来获得曲面的稀疏表示。然后,这个表面用于仔细检测线的端点。最后,通过侵蚀表面获得沟底和回冠线,同时保持端点之间的连通性。通过将ABLE与其他三种沟提取方法进行比较,验证了该方法的有效性,并使用人类连接组计划(HCP)测试-重测数据库来评估不同工具的可重复性。结果证实了ABLE是一种可靠的方法,可以准确地表示沟的拓扑结构,同时忽略吻合分支和对沟底线的高估。ABLE可通过https://github.com/HGGM-LIM/ABLE公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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