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

Abstract：

:The large amount of imaging data collected in several ongoing multi-center studies requires automated methods to delineate brain structures of interest. We have previously reported on using artificial neural networks (ANN) to define subcortical brain structures. Here we present several automated segmentation methods using multidimensional registration. A direct comparison between template, probability, artificial neural network (ANN) and support vector machine (SVM)-based automated segmentation methods is presented. Three metrics for each segmentation method are reported in the delineation of subcortical and cerebellar brain regions. Results show that the machine learning methods outperform the template and probability-based methods. Utilization of these automated segmentation methods may be as reliable as manual raters and require no rater intervention.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Powell S,Magnotta VA,Johnson H,Jammalamadaka VK,Pierson R,Andreasen NC

doi

10.1016/j.neuroimage.2007.05.063

subject

Has Abstract

pub_date

2008-01-01 00:00:00

pages

238-47

issue

1

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(07)00713-6

journal_volume

39

pub_type

杂志文章

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