Literature DB >> 15627570

Mindboggle: a scatterbrained approach to automate brain labeling.

Arno Klein1, Joy Hirsch.   

Abstract

Mindboggle (http://www.binarybottle.com/mindboggle.html) is a fully automated, feature matching approach to label cortical structures and activity anatomically in human brain MRI data. This approach does not assume that the existence of component structures and their relative spatial relationship is preserved from brain to brain, but instead disassembles a labeled atlas and reassembles its pieces to match corresponding pieces in an unlabeled subject brain before labeling. Mindboggle: (1) converts linearly coregistered subject and atlas MRI data into sulcus pieces, (2) matches each atlas piece with a combination of subject pieces by minimizing a cost function, (3) transforms atlas label boundaries to the matching subject pieces, (4) warps atlas labels to their transformed boundaries, and (5) propagates labels to fill remaining gaps in a mask derived from the subject brain. We compared Mindboggle with four registration methods: linear registration, and nonlinear registration using SPM2, AIR, and ANIMAL. Automated labeling by all of the nonlinear methods was found to be at least comparable with linear registration. Mindboggle outperformed every other method, as measured by the agreement between overlapping atlas labels and manually assigned subject labels, with respect to the union or the intersection of voxels. After applying the same procedure that Mindboggle uses to fill a subject's segmented gray matter mask with labels (step 5), the results of the other methods improved. However, after performing a one-way ANOVA (and Tukey's honestly significant difference criterion) in a multiple comparison between the results obtained by the different methods, Mindboggle was still found to be the only nonlinear method whose labeling performance was significantly better than that of linear registration or SPM2. Further advantages to Mindboggle include a high degree of robustness against image artifacts, poor image quality, and incomplete brain data. We tested the latter hypothesis by conducting all of the tests again, this time registering the atlas to an artificially lesioned version of itself, and found that Mindboggle was the only method whose performance did not degrade significantly as the lesion size increased.

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Year:  2004        PMID: 15627570     DOI: 10.1016/j.neuroimage.2004.09.016

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  33 in total

1.  Foibles, follies, and fusion: web-based collaboration for medical image labeling.

Authors:  Bennett A Landman; Andrew J Asman; Andrew G Scoggins; John A Bogovic; Joshua A Stein; Jerry L Prince
Journal:  Neuroimage       Date:  2011-08-02       Impact factor: 6.556

2.  Brain surface conformal parameterization using Riemann surface structure.

Authors:  Yalin Wang; Lok Ming Lui; Xianfeng Gu; Kiralee M Hayashi; Tony F Chan; Arthur W Toga; Paul M Thompson; Shing-Tung Yau
Journal:  IEEE Trans Med Imaging       Date:  2007-06       Impact factor: 10.048

3.  Effects of registration regularization and atlas sharpness on segmentation accuracy.

Authors:  B T Thomas Yeo; Mert R Sabuncu; Rahul Desikan; Bruce Fischl; Polina Golland
Journal:  Med Image Comput Comput Assist Interv       Date:  2007

4.  Effects of registration regularization and atlas sharpness on segmentation accuracy.

Authors:  B T Thomas Yeo; Mert R Sabuncu; Rahul Desikan; Bruce Fischl; Polina Golland
Journal:  Med Image Anal       Date:  2008-06-19       Impact factor: 8.545

5.  Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants.

Authors:  Gang Li; Li Wang; Feng Shi; Weili Lin; Dinggang Shen
Journal:  Med Image Anal       Date:  2014-06-25       Impact factor: 8.545

6.  An open source multivariate framework for n-tissue segmentation with evaluation on public data.

Authors:  Brian B Avants; Nicholas J Tustison; Jue Wu; Philip A Cook; James C Gee
Journal:  Neuroinformatics       Date:  2011-12

7.  A Bayesian approach to the creation of a study-customized neonatal brain atlas.

Authors:  Yajing Zhang; Linda Chang; Can Ceritoglu; Jon Skranes; Thomas Ernst; Susumu Mori; Michael I Miller; Kenichi Oishi
Journal:  Neuroimage       Date:  2014-07-12       Impact factor: 6.556

8.  The SRI24 multichannel atlas of normal adult human brain structure.

Authors:  Torsten Rohlfing; Natalie M Zahr; Edith V Sullivan; Adolf Pfefferbaum
Journal:  Hum Brain Mapp       Date:  2010-05       Impact factor: 5.038

9.  Reprint of "Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging".

Authors:  Kenichi Oishi; Andreia V Faria; Shoko Yoshida; Linda Chang; Susumu Mori
Journal:  Int J Dev Neurosci       Date:  2013-12-02       Impact factor: 2.457

10.  Spherical demons: fast diffeomorphic landmark-free surface registration.

Authors:  B T Thomas Yeo; Mert R Sabuncu; Tom Vercauteren; Nicholas Ayache; Bruce Fischl; Polina Golland
Journal:  IEEE Trans Med Imaging       Date:  2009-08-25       Impact factor: 10.048

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