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vol2Brain:一种用于全脑磁共振成像分析的新型在线流程

vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis.

作者信息

Manjón José V, Romero José E, Vivo-Hernando Roberto, Rubio Gregorio, Aparici Fernando, de la Iglesia-Vaya Mariam, Coupé Pierrick

机构信息

Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain.

Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain.

出版信息

Front Neuroinform. 2022 May 24;16:862805. doi: 10.3389/fninf.2022.862805. eCollection 2022.

Abstract

Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels ( > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide.

摘要

用于磁共振脑图像分析的自动且可靠的定量工具对于临床和研究环境而言都是非常宝贵的资源。在过去几年中,该领域取得了许多进展,出现了基于标签融合以及最近基于深度学习的成功技术。然而,其中很少有专门设计用于在多尺度水平上提供密集解剖标记并处理诸如白质病变(WML)等脑解剖结构改变的工具。在这项工作中,我们提出了一种用于全脑分割和分析的全自动流程(vol2Brain),它能在存在WML的情况下对大脑进行密集标记(> 100个)。这个新流程是我们之前volBrain流程的改进版本,显著扩展了可分析区域的数量。我们提出的方法基于一种快速且多尺度的多图谱标签融合技术,并具有系统误差校正功能,能够在几分钟内提供准确的体积信息。我们已将新流程部署在我们的volBrain平台(www.volbrain.upv.es)上,该平台已被证明是与全球用户分享我们技术的一种高效方式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a612/9171328/4ead2b222d08/fninf-16-862805-g0001.jpg

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