Literature DB >> 20116974

Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging.

Yasuo Kawata1, Hidetaka Arimura, Yasuo Yamashita, Taiki Magome, Masafumi Ohki, Fukai Toyofuku, Yoshiharu Higashida, Kazuhiro Tsuchiya.   

Abstract

It has been reported that the severity of subcortical vascular dementia (VaD) correlated with an area ratio of white matter hyperintensity (WMH) regions to the brain parenchyma (WMH area ratio). The purpose of this study was to develop a computer-aided evaluation method of WMH regions for diagnosis of subcortical VaD based on magnetic resonance (MR) images. A brain parenchymal region was segmented based on the histogram analysis of a T1-weigthed image. The WMH regions were segmented on the subtraction image between a T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images using two segmentation methods, i.e., a region-growing technique and a level-set method, which were automatically and adaptively selected on each WMH region based on its image features by using a support vector machine. We applied the proposed method to 33 slices of the three types of MR images with 245 lesions, which were acquired from 10 patients (age range: 64-90 years, mean: 78) with a diagnosis of VaD on a 1.5-T MR imaging scanner. The average similarity index between regions determined by a manual method and the proposed method was 93.5+/-2.0% for brain parenchymal regions and 78.2+/-11.0% for WMH regions. The WMH area ratio obtained by the proposed method correlated with that determined by two neuroradiologists with a correlation coefficient of 0.992. The results presented in this study suggest that the proposed method could assist neuroradiologists in the evaluation of WMH regions related to the subcortical VaD. 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 20116974     DOI: 10.1016/j.compmedimag.2009.12.014

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  9 in total

1.  Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images.

Authors:  Byung Il Yoo; Jung Jae Lee; Ji Won Han; San Yeo Wool Oh; Eun Young Lee; James R MacFall; Martha E Payne; Tae Hui Kim; Jae Hyoung Kim; Ki Woong Kim
Journal:  Neuroradiology       Date:  2014-02-04       Impact factor: 2.804

2.  Automated segmentation method of white matter and gray matter regions with multiple sclerosis lesions in MR images.

Authors:  Taiki Magome; Hidetaka Arimura; Shingo Kakeda; Daisuke Yamamoto; Yasuo Kawata; Yasuo Yamashita; Yoshiharu Higashida; Fukai Toyofuku; Masafumi Ohki; Yukunori Korogi
Journal:  Radiol Phys Technol       Date:  2010-09-30

3.  Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging.

Authors:  Kay C Igwe; Patrick J Lao; Robert S Vorburger; Arit Banerjee; Andres Rivera; Anthony Chesebro; Krystal Laing; Jennifer J Manly; Adam M Brickman
Journal:  Magn Reson Imaging       Date:  2021-10-16       Impact factor: 2.546

Review 4.  Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review.

Authors:  Maria Eugenia Caligiuri; Paolo Perrotta; Antonio Augimeri; Federico Rocca; Aldo Quattrone; Andrea Cherubini
Journal:  Neuroinformatics       Date:  2015-07

5.  BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities.

Authors:  Ludovica Griffanti; Giovanna Zamboni; Aamira Khan; Linxin Li; Guendalina Bonifacio; Vaanathi Sundaresan; Ursula G Schulz; Wilhelm Kuker; Marco Battaglini; Peter M Rothwell; Mark Jenkinson
Journal:  Neuroimage       Date:  2016-07-09       Impact factor: 6.556

6.  White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks.

Authors:  R Guerrero; C Qin; O Oktay; C Bowles; L Chen; R Joules; R Wolz; M C Valdés-Hernández; D A Dickie; J Wardlaw; D Rueckert
Journal:  Neuroimage Clin       Date:  2017-12-20       Impact factor: 4.881

7.  Multi-atlas based detection and localization (MADL) for location-dependent quantification of white matter hyperintensities.

Authors:  Dan Wu; Marilyn Albert; Anja Soldan; Corinne Pettigrew; Kenichi Oishi; Yusuke Tomogane; Chenfei Ye; Ting Ma; Michael I Miller; Susumu Mori
Journal:  Neuroimage Clin       Date:  2019-03-13       Impact factor: 4.881

8.  Detection of subtle white matter lesions in MRI through texture feature extraction and boundary delineation using an embedded clustering strategy.

Authors:  Kokhaur Ong; David M Young; Sarina Sulaiman; Siti Mariyam Shamsuddin; Norzaini Rose Mohd Zain; Hilwati Hashim; Kahhay Yuen; Stephan J Sanders; Weimiao Yu; Seepheng Hang
Journal:  Sci Rep       Date:  2022-03-15       Impact factor: 4.379

9.  A deep semantic segmentation correction network for multi-model tiny lesion areas detection.

Authors:  Yue Liu; Xiang Li; Tianyang Li; Bin Li; Zhensong Wang; Jie Gan; Benzheng Wei
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-30       Impact factor: 2.796

  9 in total

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