TY - GEN
T1 - Lesion localization in paediatric epilepsy using patch-based convolutional neural network
AU - Aminpour, Azad
AU - Ebrahimi, Mehran
AU - Widjaja, Elysa
N1 - Funding Information:
This research was conducted with the support of EpLink – The Epilepsy Research Program of the Ontario Brain Institute (OBI). The opinions, results and conclusions are those of the authors and no endorsement by the OBI is intended or should be inferred. This research was also supported in part by an NSERC Discovery Grant for M.E. AA would like to acknowledge Ontario Tech university for a doctoral graduate international tuition scholarship (GITS). The authors gratefully acknowledge the support of NVIDIA Corporation for the donation of GPUs used in this research through its Academic Grant Program.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Focal Cortical Dysplasia (FCD) is one of the most common causes of paediatric medically intractable focal epilepsy. In cases of medically resistant epilepsy, surgery is the best option to achieve a seizure-free condition. Pre-surgery lesion localization affects the surgery outcome. Lesion localization is done through examining the MRI for FCD features, but the MRI features of FCD can be subtle and may not be detected by visual inspection. Patients with epilepsy who have normal MRI are considered to have MRI-negative epilepsy. Recent advances in machine learning and deep learning hold the potential to improve the detection and localization of FCD without the need to conduct extensive pre-processing and FCD feature extraction. In this research, we apply Convolutional Neural Networks (CNNs) to classify FCD in children with focal epilepsy and localize the lesion. Two networks are presented here, the first network is applied on the whole-slice of the MR images, and the second network is taking smaller patches extracted from the slices of each MRI as input. The patch-wise model successfully classifies all healthy patients (13 out of 13), while 12 out of 13 cases are correctly identified by the whole-slice model. Using the patch-wise model, we identified the lesion in 17 out of 17 MR-positive subjects with coverage of 85% and for MR-negative subjects, we identify 11 out of 13 FCD subjects with lesion coverage of 66%. The findings indicate that convolutional neural network is a promising tool to objectively identify subtle lesions such as FCD in children with focal epilepsy.
AB - Focal Cortical Dysplasia (FCD) is one of the most common causes of paediatric medically intractable focal epilepsy. In cases of medically resistant epilepsy, surgery is the best option to achieve a seizure-free condition. Pre-surgery lesion localization affects the surgery outcome. Lesion localization is done through examining the MRI for FCD features, but the MRI features of FCD can be subtle and may not be detected by visual inspection. Patients with epilepsy who have normal MRI are considered to have MRI-negative epilepsy. Recent advances in machine learning and deep learning hold the potential to improve the detection and localization of FCD without the need to conduct extensive pre-processing and FCD feature extraction. In this research, we apply Convolutional Neural Networks (CNNs) to classify FCD in children with focal epilepsy and localize the lesion. Two networks are presented here, the first network is applied on the whole-slice of the MR images, and the second network is taking smaller patches extracted from the slices of each MRI as input. The patch-wise model successfully classifies all healthy patients (13 out of 13), while 12 out of 13 cases are correctly identified by the whole-slice model. Using the patch-wise model, we identified the lesion in 17 out of 17 MR-positive subjects with coverage of 85% and for MR-negative subjects, we identify 11 out of 13 FCD subjects with lesion coverage of 66%. The findings indicate that convolutional neural network is a promising tool to objectively identify subtle lesions such as FCD in children with focal epilepsy.
KW - Convolutional Neural Network
KW - Deep learning
KW - Focal Cortical Dysplasia
KW - Patch-based
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U2 - 10.1007/978-3-030-50516-5_19
DO - 10.1007/978-3-030-50516-5_19
M3 - Conference contribution
AN - SCOPUS:85087279069
SN - 9783030505158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 216
EP - 227
BT - Image Analysis and Recognition - 17th International Conference, ICIAR 2020, Proceedings
A2 - Campilho, Aurélio
A2 - Karray, Fakhri
A2 - Wang, Zhou
PB - Springer
T2 - 17th International Conference on Image Analysis and Recognition, ICIAR 2020
Y2 - 24 June 2020 through 26 June 2020
ER -