Literature DB >> 9245652

An automated registration algorithm for measuring MRI subcortical brain structures.

D V Iosifescu1, M E Shenton, S K Warfield, R Kikinis, J Dengler, F A Jolesz, R W McCarley.   

Abstract

An automated registration algorithm was used to elastically match an anatomical magnetic resonance (MR) atlas onto individual brain MR images. Our goal was to evaluate the accuracy of this procedure for measuring the volume of MRI brain structures. We applied two successive algorithms to a series of 28 MR brain images, from 14 schizophrenia patients and 14 normal controls. First, we used an automated segmentation program to differentiate between white matter, cortical and subcortical gray matter, and cerebrospinal fluid. Next, we elastically deformed the atlas segmentation to fit the subject's brain, by matching the white matter and subcortical gray matter surfaces. To assess the accuracy of these measurements, we compared, on all 28 images, 11 brain structures, measured with elastic matching, with the same structures traced manually on MRI scans. The similarity between the measurements (the relative difference between the manual and the automated volume) was 97% for whole white matter, 92% for whole gray matter, and on average 89% for subcortical structures. The relative spatial overlap between the manual and the automated volumes was 97% for whole white matter, 92% for whole gray matter, and on average 75% for subcortical structures. For all pairs of structures rendered with the automated and the manual method, Pearson correlations were between r = 0.78 and r = 0.98 (P < 0.01, N = 28), except for globus pallidus, where r = 0.55 (left) and r = 0. 44 (right) (P < 0.01, N = 28). In the schizophrenia group, compared to the controls, we found a 16.7% increase in MRI volume for the basal ganglia (i.e., caudate nucleus, putamen, and globus pallidus), but no difference in total gray/white matter volume or in thalamic MR volume. This finding reproduces previously reported results, obtained in the same patient population with manually drawn structures, and suggests the utility/efficacy of our automated registration algorithm over more labor-intensive manual tracings.

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Year:  1997        PMID: 9245652     DOI: 10.1006/nimg.1997.0274

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


  30 in total

Review 1.  MRI anatomy of schizophrenia.

Authors:  R W McCarley; C G Wible; M Frumin; Y Hirayasu; J J Levitt; I A Fischer; M E Shenton
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2.  A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder.

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3.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.

Authors:  Simon K Warfield; Kelly H Zou; William M Wells
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4.  MR imaging anatomy in neurodegeneration: a robust volumetric parcellation method of the frontal lobe gyri with quantitative validation in patients with dementia.

Authors:  B Iordanova; D Rosenbaum; D Norman; M Weiner; C Studholme
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5.  Reliability and validity of MRI-based automated volumetry software relative to auto-assisted manual measurement of subcortical structures in HIV-infected patients from a multisite study.

Authors:  Jeffrey Dewey; George Hana; Troy Russell; Jared Price; Daniel McCaffrey; Jaroslaw Harezlak; Ekta Sem; Joy C Anyanwu; Charles R Guttmann; Bradford Navia; Ronald Cohen; David F Tate
Journal:  Neuroimage       Date:  2010-03-22       Impact factor: 6.556

6.  Using automated morphometry to detect associations between ERP latency and structural brain MRI in normal adults.

Authors:  Valerie A Cardenas; Linda L Chao; Rob Blumenfeld; Enmin Song; Dieter J Meyerhoff; Michael W Weiner; Colin Studholme
Journal:  Hum Brain Mapp       Date:  2005-07       Impact factor: 5.038

7.  Neocortical gray matter volume in first-episode schizophrenia and first-episode affective psychosis: a cross-sectional and longitudinal MRI study.

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Journal:  Biol Psychiatry       Date:  2007-06-27       Impact factor: 13.382

8.  Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures.

Authors:  Stephanie Powell; Vincent A Magnotta; Hans Johnson; Vamsi K Jammalamadaka; Ronald Pierson; Nancy C Andreasen
Journal:  Neuroimage       Date:  2007-08-22       Impact factor: 6.556

9.  Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation.

Authors:  Yongfu Hao; Tianyao Wang; Xinqing Zhang; Yunyun Duan; Chunshui Yu; Tianzi Jiang; Yong Fan
Journal:  Hum Brain Mapp       Date:  2013-10-23       Impact factor: 5.038

10.  Effects of spatial transformation on regional brain volume estimates.

Authors:  John S Allen; Joel Bruss; Sonya Mehta; Thomas Grabowski; C Kice Brown; Hanna Damasio
Journal:  Neuroimage       Date:  2008-06-03       Impact factor: 6.556

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