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Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

PURPOSE: Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD throug...

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Autores principales: Feng, Cuixia, Zhao, Hulin, Li, Yueer, Wen, Junhai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497927/
https://www.ncbi.nlm.nih.gov/pubmed/32809276
http://dx.doi.org/10.1002/acm2.12985
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author Feng, Cuixia
Zhao, Hulin
Li, Yueer
Wen, Junhai
author_facet Feng, Cuixia
Zhao, Hulin
Li, Yueer
Wen, Junhai
author_sort Feng, Cuixia
collection PubMed
description PURPOSE: Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid‐attenuated inversion recovery (FLAIR)‐negative lesions using convolutional neural network (CNN) technology. METHODS: The technique involves training a six‐layer CNN named Net‐Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net‐Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR‐negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm. RESULTS: The PIBs most similar to an FCD lesion image block were identified by the trained Net‐Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR‐negative lesion images from 12 patients. The subject‐wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68. CONCLUSION: We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR‐negative FCD lesions. This work is the first study to apply a CNN‐based model to detect and segment FCD lesions in images of FLAIR‐negative lesions.
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spelling pubmed-74979272020-09-25 Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network Feng, Cuixia Zhao, Hulin Li, Yueer Wen, Junhai J Appl Clin Med Phys Medical Imaging PURPOSE: Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid‐attenuated inversion recovery (FLAIR)‐negative lesions using convolutional neural network (CNN) technology. METHODS: The technique involves training a six‐layer CNN named Net‐Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net‐Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR‐negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm. RESULTS: The PIBs most similar to an FCD lesion image block were identified by the trained Net‐Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR‐negative lesion images from 12 patients. The subject‐wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68. CONCLUSION: We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR‐negative FCD lesions. This work is the first study to apply a CNN‐based model to detect and segment FCD lesions in images of FLAIR‐negative lesions. John Wiley and Sons Inc. 2020-08-18 /pmc/articles/PMC7497927/ /pubmed/32809276 http://dx.doi.org/10.1002/acm2.12985 Text en © 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Medical Imaging
Feng, Cuixia
Zhao, Hulin
Li, Yueer
Wen, Junhai
Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network
title Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network
title_full Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network
title_fullStr Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network
title_full_unstemmed Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network
title_short Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network
title_sort automatic localization and segmentation of focal cortical dysplasia in flair‐negative patients using a convolutional neural network
topic Medical Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7497927/
https://www.ncbi.nlm.nih.gov/pubmed/32809276
http://dx.doi.org/10.1002/acm2.12985
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