Literature DB >> 12482068

Manual and semiautomated measurement of cerebellar subregions on MR images.

Ronald Pierson1, Patricia Westmoreland Corson, Lonnie L Sears, Daniel Alicata, Vincent Magnotta, Daniel Oleary, Nancy C Andreasen.   

Abstract

Previous structural and functional imaging studies suggest that the corticocerebellar-thalamic-cortical circuit is dysfunctional in schizophrenia. Accurate identification and volumetric measurement of cerebellar subregions are essential to the assessment of the cerebellum's role in healthy and disease states. Manual parcellation of the cerebellum on MR images was performed with the use of guide traces. Guide traces identified relevant fissures and borders in several planes, and their intersections with the primary tracing plane were used to maintain consistency and accuracy during the parcellation. The cerebellum was parcellated into right and left anterior lobes, superior posterior lobes, inferior posterior lobes, and corpus medullare. A systematic review of the final traces ensured their accuracy. An artificial neural network was trained using a novel landmark-warped method to help account for wide variability in structure size and location. Overlaps of the manually traced lobes (intersection/union) ranged from 0.78 to 0.85 and intraclass correlations (r2) ranged from 0.82 to 0.94. In a comparison of the semiautomated method with the manual method overlaps ranged from 0.83 to 0.88 and intraclass correlations ranged from 0.92 to 0.97. For two raters using the semiautomated method overlaps ranged from 0.83 to 0.88 and intraclass correlations ranged from 0.97 to 0.99. The semiautomated method was built on the groundwork of the manual method to produce more reliable results in a fraction of the time, making valid measurements possible on a large number of subjects.

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Year:  2002        PMID: 12482068     DOI: 10.1006/nimg.2002.1207

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


  31 in total

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2.  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

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

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4.  Cerebellar volume and cognitive functioning in children who experienced early deprivation.

Authors:  Patrick M Bauer; Jamie L Hanson; Ronald K Pierson; Richard J Davidson; Seth D Pollak
Journal:  Biol Psychiatry       Date:  2009-08-05       Impact factor: 13.382

5.  The nature and extent of cerebellar atrophy in chronic temporal lobe epilepsy.

Authors:  Temitayo O Oyegbile; Katherine Bayless; Kevin Dabbs; Jana Jones; Paul Rutecki; Ronald Pierson; Michael Seidenberg; Bruce Hermann
Journal:  Epilepsia       Date:  2011-01-26       Impact factor: 5.864

6.  Approaching expert results using a hierarchical cerebellum parcellation protocol for multiple inexpert human raters.

Authors:  John A Bogovic; Bruno Jedynak; Rachel Rigg; Annie Du; Bennett A Landman; Jerry L Prince; Sarah H Ying
Journal:  Neuroimage       Date:  2012-09-04       Impact factor: 6.556

7.  Abnormal cerebellar structure is dependent on phenotype of isolated cleft of the lip and/or palate.

Authors:  Ian DeVolder; Lynn Richman; Amy L Conrad; Vincent Magnotta; Peg Nopoulos
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8.  Rapid automatic segmentation of the human cerebellum and its lobules (RASCAL)--implementation and application of the patch-based label-fusion technique with a template library to segment the human cerebellum.

Authors:  Katrin Weier; Vladimir Fonov; Karyne Lavoie; Julien Doyon; D Louis Collins
Journal:  Hum Brain Mapp       Date:  2014-04-28       Impact factor: 5.038

9.  Brain atrophy associated with baseline and longitudinal measures of cognition.

Authors:  V A Cardenas; L L Chao; C Studholme; K Yaffe; B L Miller; C Madison; S T Buckley; D Mungas; N Schuff; M W Weiner
Journal:  Neurobiol Aging       Date:  2009-05-14       Impact factor: 4.673

10.  Cerebellar volume in offspring from multiplex alcohol dependence families.

Authors:  Shirley Y Hill; Srirangam Muddasani; Konasale Prasad; Jeffrey Nutche; Stuart R Steinhauer; Joelle Scanlon; Michael McDermott; Matcheri Keshavan
Journal:  Biol Psychiatry       Date:  2006-03-14       Impact factor: 13.382

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