TY - GEN
T1 - Automatic detection of subtle focal cortical dysplasia using surface-based features on MRI
AU - Besson, Pierre
AU - Colliot, Olivier
AU - Evans, Alan
AU - Bernasconi, Andrea
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Biomedical image processing
KW - Biomedical signal detection
KW - Magnetic resonance imaging
KW - Nervous system
KW - Neural network applications
UR - http://www.scopus.com/inward/record.url?scp=51049121350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51049121350&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2008.4541326
DO - 10.1109/ISBI.2008.4541326
M3 - Conference contribution
AN - SCOPUS:51049121350
SN - 9781424420032
T3 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
SP - 1633
EP - 1636
BT - 2008 5th IEEE International Symposium on Biomedical Imaging
T2 - 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
Y2 - 14 May 2008 through 17 May 2008
ER -