Automatic detection of subtle focal cortical dysplasia using surface-based features on MRI

Pierre Besson*, Olivier Colliot, Alan Evans, Andrea Bernasconi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Focal cortical dysplasia (FCD) is an important cause of pharmacoresistant epilepsy. Small FCD lesions are difficult to distinguish from non-lesional cortex and remain often overlooked on radiological MRI inspection. This paper presents a method to detect small FCD lesions on T1-MRI relying on surface-based features: cortical thickness, gradient magnitude at the white-matter / grey-matter interface, cortical signal intensity, curvature and depth of inner-cortical surface. These features best describe the visual and morphometric characteristics of small FCD, and allow differentiating it from healthy tissues. The automatic detection was performed by a neural-network bagging trained on manual labels. The method was tested on 19 patients with small FCD and identified the lesion in 89% (17/19) of cases. Cluster analysis demonstrated that the lesional cluster was the largest in 76% (13/17) of identified cases. This new approach may assist the presurgical evaluation of patients with intractable epilepsy, especially those with "MRI-negative" epilepsy.

Original languageEnglish (US)
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages1633-1636
Number of pages4
DOIs
StatePublished - 2008
Externally publishedYes
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: May 14 2008May 17 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Country/TerritoryFrance
CityParis
Period5/14/085/17/08

Keywords

  • Biomedical image processing
  • Biomedical signal detection
  • Magnetic resonance imaging
  • Nervous system
  • Neural network applications

ASJC Scopus subject areas

  • Biomedical Engineering

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