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

Autor:	Manjón JV; Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain., Romero JE; Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Valencia, Spain., Vivo-Hernando R; Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain., Rubio G; Departamento de Matemática Aplicada, Universitat Politècnica de València, Valencia, Spain., Aparici F; Área de Imagen Medica, Hospital Universitario y Politécnico La Fe, Valencia, Spain., de la Iglesia-Vaya M; Unidad Mixta de Imagen Biomédica FISABIO-CIPF, Fundación Para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana, Valencia, Spain.; Centro de Investigación Biomédica en Red de Salud Mental, ISC III, València, Spain., Coupé P; Centre National de la Recherche Scientifique, Univ. Bordeaux, Bordeaux INP, Laboratoire Bordelais de Recherche en Informatique, UMR5800, PICTURA, Talence, France.
Jazyk:	angličtina
Zdroj:	Frontiers in neuroinformatics [Front Neuroinform] 2022 May 24; Vol. 16, pp. 862805. Date of Electronic Publication: 2022 May 24 (Print Publication: 2022).
DOI:	10.3389/fninf.2022.862805
Abstrakt:	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 ( N > 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Manjón, Romero, Vivo-Hernando, Rubio, Aparici, de la Iglesia-Vaya and Coupé.)
Databáze:	MEDLINE
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