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

vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis

José V Manjón  1 , José E Romero  1 , Roberto Vivo-Hernando  2 , Gregorio Rubio  3 , Fernando Aparici  4 , Mariam de la Iglesia-Vaya  5   6 , Pierrick Coupé  7
Affiliations

vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis

José V Manjón et al. Front Neuroinform. .

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 (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.

Keywords: MRI; analysis; brain; cloud; segmentation.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Example of cGM tissue correction. From right to left: Reference T1 image, original cGM map, corrected cGM map, and map of changes (white means inclusion and black means removal of pf voxels). In the bottom row, a close up is shown to better highlight the differences.
Figure 2
Figure 2
Top row shows the original labeling and bottom row shows the corrected labeling. Note that the external CSF label has been added to the labeling protocol.
Figure 3
Figure 3
vol2Brain pipeline scheme. In the first row, the preprocessing for any new subject is presented. In the second row, the results of the ICC extraction, structure, and tissue segmentations jointly with the cortical thickness estimation are presented. Finally, in the third row, the volumetric information is extracted and presented.
Figure 4
Figure 4
Example results of vol2Brain. T1 image, ICC mask, brain tissues, lobes, and structures.
See this image and copyright information in PMC

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References

    1. Arnold J. B., Liow J. S., Schaper K. A., Stern J. J., Sled J. G., Shattuck D. W., et al. . (2001). Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. Neuroimage 13, 931–943. 10.1006/nimg.2001.0756 - DOI - PubMed
    1. Ashburner J., Friston K. J. (2005). Unified segmentation. Neuroimage 26, 839–851. 10.1016/j.neuroimage.2005.02.018 - DOI - PubMed
    1. Avants B. B., Epstein C. L., Grossman M., Gee J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 10.1016/j.media.2007.06.004 - DOI - PMC - PubMed
    1. Bazin P. L., Pham D. L. (2008). Homeomorphic brain image segmentation with topological and statistical atlases. Med. Image Anal. 12, 616–625. 10.1016/j.media.2008.06.008 - DOI - PMC - PubMed
    1. Carass A., Cuzzocreo J. L., Han S., Hernandez-Castillo C. R., Rasser P. E., Ganz M., et al. . (2018). Comparing fully automated state-of-the-art cerebellum parcellation from Magnetic Resonance Imaging. Neuroimage 183, 150–172. 10.1016/j.neuroimage.2018.08.003 - DOI - PMC - PubMed