Segmentation Of Magnetic Resonance Images Using A Back Propagation Artificial Neural Network

Piraino, David W.; Sundar, Srinivasaraghavan; Richmond, Bradford J.; Schils, Jean; Thome, J. M.

doi:10.1109/iembs.1991.684549

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…impedance tomograms to determine cardiopulmonary function [36][37][38][39]. In fact, images need not be radiographs at all, and computed image analysis has been applied to histologic images.…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence in medicine and male infertility

Lamb

Niederberger

1993

World J Urol

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Section: Diagnosismentioning

confidence: 99%

Artificial intelligence in medicine and male infertility

Lamb

Niederberger

1993

World J Urol

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“…In addition to the regional segmentation using neural networks described above, neural network based machine learning algorithms have been used in MR image analysis for approximately 20 years. The primary utilization of neural networks has been in the area of tissue classification (Piraino et al, 1991;Clarke et al, 1993;Cagnoni et al, 1993;Lin et al, 1996a;Lin et al, 1996b;Reddick et al, 1997;Mulhern et al, 1999;Perez et al, 2003;Glass et al, 2003;Valdes-Cristerna et al, 2004;Wagenknecht et al, 2004;Shen et al, 2005;Wismuller et al, 2006, Song et al, 2006. Both supervised backpropogation as well as self organizing maps have been utilized for this purpose.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2008

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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.

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“…Arguably, it has been of most use in the unsupervised classification of structural and functional neuroimaging data. Adequate classification of brain structure imaged using MRI has been reported for intermediate but not T2-weighted images [9] as well as for accurate estimates of grey/white matter volume [10]. In the latter study, there were differences of less than 2% for grey matter and 6% for white matter compared to experienced manual raters.…”

Section: Introductionmentioning

confidence: 62%

Self-Organizing Neural Network Analyses of Cardiac Data in Depression

et al. 2004

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Objective: To determine if an unsupervised self-organizing neural network could create a clinically meaningful distinction of ‘depression’ versus ‘no depression’ based on cardiac time-series data. Design: A self-organizing map (SOM) was used to separate the time-series of 84 subjects into groups based on characteristics of the data alone. Materials and Methods: Analyses included natural log transformations and two types of filtering to enhance characteristics of the data as well as classifications of unprocessed data. A Pearson χ² analysis was performed to determine if the SOM groups bore any relation to the binary clinical groups. Results: Overall correct SOM classifications ranged from 54 to 70.2% with two classifications being clinically meaningful. Conclusions: SOM classifications of cardiac time-series data with enhanced ultradian variations and cardiac data recorded around the interval when a person was in bed were useful in differentiating clinically meaningful subgroups with and without depression.

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Segmentation Of Magnetic Resonance Images Using A Back Propagation Artificial Neural Network

Cited by 4 publications

References 2 publications

Artificial intelligence in medicine and male infertility

Artificial intelligence in medicine and male infertility

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

Self-Organizing Neural Network Analyses of Cardiac Data in Depression

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