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高度连接的开口编码在最大树结构中：在脑 MRI T1 中的颅骨剥离中的应用。

Hyperconnected Openings Codified in a Max Tree Structure: An Application for Skull-Stripping in Brain MRI T1.

机构信息

Conacyt-Centro de Investigaciones en Óptica, Aguascalientes 20200, Mexico.

Facultad de Ingeniería, Universidad Autónoma de Querétaro, Santiago de Querétaro 76010, Mexico.

出版信息

Sensors (Basel). 2022 Feb 11;22(4):1378. doi: 10.3390/s22041378.

DOI:10.3390/s22041378
PMID:35214280
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8962990/
Abstract

This article presents two procedures involving a maximal hyperconnected function and a hyperconnected lower leveling to segment the brain in a magnetic resonance imaging T1 weighted using new openings on a max-tree structure. The openings are hyperconnected and are viscous transformations. The first procedure considers finding the higher hyperconnected maximum by using an increasing criterion that plays a central role during segmentation. The second procedure utilizes hyperconnected lower leveling, which acts as a marker, controlling the reconstruction process into the mask. As a result, the proposal allows an efficient segmentation of the brain to be obtained. In total, 38 magnetic resonance T1-weighted images obtained from the Internet Brain Segmentation Repository are segmented. The Jaccard and Dice indices are computed, compared, and validated with the efficiency of the Brain Extraction Tool software and other algorithms provided in the literature.

摘要

本文提出了两种方法，涉及到最大超连通函数和超连通下水平化，用于对磁共振成像 T1 加权图像进行分割，这些方法利用了 max-tree 结构上的新开口进行操作。这些开口是超连通的，并且是粘性变换。第一个方法通过使用一个递增的标准来寻找更高的超连通最大值，该标准在分割过程中起着核心作用。第二个方法利用超连通下水平化作为标记，控制重建过程到掩模中。结果，该方法允许有效地分割大脑。总共对来自互联网大脑分割库的 38 张磁共振 T1 加权图像进行了分割。计算了 Jaccard 和 Dice 指数，并与 Brain Extraction Tool 软件和文献中提供的其他算法的效率进行了比较和验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2f44/8962990/1141fb5ee30a/sensors-22-01378-g015.jpg
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