Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons.
Details
Serval ID
serval:BIB_0191E5FC5C19
Type
A part of a book
Publication sub-type
Chapter: chapter ou part
Collection
Publications
Institution
Title
Epileptogenic lesion quantification in MRI using contralateral 3D texture comparisons.
Title of the book
Medical image computing and computer-assisted intervention
Publisher
Medical Image Computing and Computer-Assisted Intervention
Publication state
Published
Issued date
2013
Peer-reviewed
Oui
Volume
16
Number
Pt 2
Pages
353-360
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Publication Status: ppublish
Abstract
Epilepsy is a disorder of the brain that can lead to acute crisis and temporary loss of brain functions. Surgery is used to remove focal lesions that remain resistant to treatment. An accurate localization of epileptogenic lesions has a strong influence on the outcome of epilepsy surgery. Magnetic resonance imaging (MRI) is clinically used for lesion detection and treatment planning, mainly through simple visual analysis. However, visual inspection in MRI can be highly subjective and subtle 3D structural abnormalities are not always entirely removed during surgery. In this paper, we introduce a lesion abnormality score based on computerized comparison of the 3D texture properties between brain hemispheres in T1 MRI. Overlapping cubic texture blocks extracted from user-defined 3D regions of interest (ROI) are expressed in terms of energies of 3D steerable Riesz wavelets. The abnormality score is defined as the Hausdorff distance between the ROI and its corresponding contralateral region in the brain, both expressed as ensembles of blocks in the feature space. A classification based on the proposed score allowed an accuracy of 85% with 10 control subjects and 8 patients with epileptogenic lesions. The approach therefore constitutes a valuable tool for the objective pre-surgical evaluation of patients undergoing epilepsy surgery.
Keywords
Algorithms, Artificial Intelligence, Brain/pathology, Epilepsy/pathology, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Imaging, Three-Dimensional/methods, Magnetic Resonance Imaging/methods, Nerve Net/pathology, Pattern Recognition, Automated/methods, Reproducibility of Results, Sensitivity and Specificity
Pubmed
Web of science
Open Access
Yes
Create date
29/08/2023 7:44
Last modification date
10/10/2023 13:37