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Computer aided FCD lesion detection based on T1 MRI data

Xiaoxia Qu, Ljiljana Platisa UGent, Bart Goossens UGent, Tingzhu Bai, Karel Deblaere UGent and Wilfried Philips UGent (2015) Medical Image Perception Conference XVI, Abstracts.
abstract
Focal cortical dysplasia (FCD) is a frequent cause of epilepsy and can be detected using brain magnetic resonance imaging (MRI). The FCD lesions in MRI images are characterized by blurring of the gray matter/white matter (GM/WM) junction, cortical thickening and hyper-intensity signal within lesional region compared with other cortical regions. However, detecting FCD lesions by means of visual inspection can be a very difficult task for radiologists because the lesions are very subtle. To assist physicians in detecting the FCD lesions more efficiently and reduce the false positive regions resulted from the existing methods, we propose an algorithm for automated FCD detection based on T1 MRI data.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
MRI, classification., detection, FCD
in
Medical Image Perception Conference XVI, Abstracts
article number
34
pages
1 pages
publisher
Medical Image Perception Society
conference name
Medical Image Perception Conference XVI
conference location
Ghent, Belgium
conference start
2015-06-03
conference end
2015-06-05
language
English
UGent publication?
yes
classification
C3
copyright statement
I have retained and own the full copyright for this publication
id
6847765
handle
http://hdl.handle.net/1854/LU-6847765
date created
2015-06-24 15:59:10
date last changed
2016-12-21 15:41:07
@inproceedings{6847765,
  abstract     = {Focal cortical dysplasia (FCD) is a frequent cause of epilepsy and can be detected using brain magnetic resonance imaging (MRI). The FCD lesions in MRI images are characterized by blurring of the gray matter/white matter (GM/WM) junction, cortical thickening and hyper-intensity signal within lesional region compared with other cortical regions. However, detecting FCD lesions by means of visual inspection can be a very difficult task for radiologists because the lesions are very subtle. To assist physicians in detecting the FCD lesions more efficiently and reduce the false positive regions resulted from the existing methods, we propose an algorithm for automated FCD detection based on T1 MRI data.},
  articleno    = {34},
  author       = {Qu, Xiaoxia and Platisa, Ljiljana and Goossens, Bart and Bai, Tingzhu and Deblaere, Karel and Philips, Wilfried},
  booktitle    = {Medical Image Perception Conference XVI, Abstracts},
  keyword      = {MRI,classification.,detection,FCD},
  language     = {eng},
  location     = {Ghent, Belgium},
  pages        = {1},
  publisher    = {Medical Image Perception Society},
  title        = {Computer aided FCD lesion detection based on T1 MRI data},
  year         = {2015},
}

Chicago
Qu, Xiaoxia, Ljiljana Platisa, Bart Goossens, Tingzhu Bai, Karel Deblaere, and Wilfried Philips. 2015. “Computer Aided FCD Lesion Detection Based on T1 MRI Data.” In Medical Image Perception Conference XVI, Abstracts. Medical Image Perception Society.
APA
Qu, X., Platisa, L., Goossens, B., Bai, T., Deblaere, K., & Philips, W. (2015). Computer aided FCD lesion detection based on T1 MRI data. Medical Image Perception Conference XVI, Abstracts. Presented at the Medical Image Perception Conference XVI, Medical Image Perception Society.
Vancouver
1.
Qu X, Platisa L, Goossens B, Bai T, Deblaere K, Philips W. Computer aided FCD lesion detection based on T1 MRI data. Medical Image Perception Conference XVI, Abstracts. Medical Image Perception Society; 2015.
MLA
Qu, Xiaoxia, Ljiljana Platisa, Bart Goossens, et al. “Computer Aided FCD Lesion Detection Based on T1 MRI Data.” Medical Image Perception Conference XVI, Abstracts. Medical Image Perception Society, 2015. Print.