Clustering-Based Undersampling to Support Automatic Detection of Focal Cortical Dysplasias

Hoyos-Osorio, Keider; Alvarez, Azucena L.; Orozco, Álvaro A.; Ríos, J. I; Daza-Santacoloma, Genaro

doi:10.1007/978-3-319-75193-1_36

Cited by 3 publications

(1 citation statement)

References 9 publications

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“… 8 We have previously developed an openly available, robust, and replicated surface‐based method to identify focal cortical dysplasias (FCDs). 9 , 10 , 11 , 12 However, most previous studies were based on cohorts with histologically or radiologically confirmed FCDs, which do not fully capture the complexity and heterogeneity of diagnostically inconclusive patients who present for presurgical evaluation by the MDT.…”

Section: Introductionmentioning

confidence: 99%

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

et al. 2020

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Objective This retrospective, cross‐sectional study evaluated the feasibility and potential benefits of incorporating deep‐learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug‐resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. Methods A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross‐validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier‐predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier‐predicted lesions was considered colocalization. Results In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted. Significance There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep‐learning–based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories.

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Section: Introductionmentioning

confidence: 99%

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

et al. 2020

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An Enhanced Sequential Search Feature Selection Based on mRMR to Support FCD Localization

Castañeda-Gonzalez

Álvarez-Meza

Gutiérrez

2019

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Planning sEEG implantation using automated lesion detection: retrospective feasibility study

Wagstyl

Adler

Pimpel

et al. 2019

Preprint

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Objective: A retrospective, cross-sectional study to evaluate the feasibility and potential benefits of incorporating deep-learning on structural MRI into planning stereoelectroencephalography (sEEG) implantation in paediatric patients with diagnostically complex drug-resistant epilepsy. This study aims to assess the degree of co-localisation between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. Methods: A neural network classifier was applied to cortical features from MRI data from three cohorts. 1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). 2) Specificity was assessed in 20 paediatric healthy controls. 3) Feasibility for incorporation into sEEG implantation plans was evaluated in 38 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10mm between SOZ contacts and classifier-predicted lesions was considered co-localisation. Results: In patients with radiologically-defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity 100%). Of 34 sEEG patients, 21 patients had a focal cortical SOZ. Of these there was co-localisation between classifier output and SOZ contacts in 62%. The algorithm detected 7/8 histopathologically-confirmed FCDs (86%). Conclusions: There was a high degree of co-localisation between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning based MRI lesion detection into sEEG implantation planning. Our findings demonstrate that automated MRI analysis could be used to plan optimal electrode trajectories.

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Clustering-Based Undersampling to Support Automatic Detection of Focal Cortical Dysplasias

Cited by 3 publications

References 9 publications

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study

An Enhanced Sequential Search Feature Selection Based on mRMR to Support FCD Localization

Planning sEEG implantation using automated lesion detection: retrospective feasibility study

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