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Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images

Convolutional neural network (CNN) models are of great promise to aid the segmentation and analysis of brain structures. Here, we tested whether CNN trained to segment normal optic chiasms from the T1w magnetic resonance imaging (MRI) image can be also applied to abnormal chiasms, specifically with...

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Autores principales: Puzniak, Robert J., Prabhakaran, Gokulraj T., Hoffmann, Michael B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573410/
https://www.ncbi.nlm.nih.gov/pubmed/34759795
http://dx.doi.org/10.3389/fnins.2021.755785
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author Puzniak, Robert J.
Prabhakaran, Gokulraj T.
Hoffmann, Michael B.
author_facet Puzniak, Robert J.
Prabhakaran, Gokulraj T.
Hoffmann, Michael B.
author_sort Puzniak, Robert J.
collection PubMed
description Convolutional neural network (CNN) models are of great promise to aid the segmentation and analysis of brain structures. Here, we tested whether CNN trained to segment normal optic chiasms from the T1w magnetic resonance imaging (MRI) image can be also applied to abnormal chiasms, specifically with optic nerve misrouting as typical for human albinism. We performed supervised training of the CNN on the T1w images of control participants (n = 1049) from the Human Connectome Project (HCP) repository and automatically generated algorithm-based optic chiasm masks. The trained CNN was subsequently tested on data of persons with albinism (PWA; n = 9) and controls (n = 8) from the CHIASM repository. The quality of outcome segmentation was assessed via the comparison to manually defined optic chiasm masks using the Dice similarity coefficient (DSC). The results revealed contrasting quality of masks obtained for control (mean DSC ± SEM = 0.75 ± 0.03) and PWA data (0.43 ± 0.8, few-corrected p = 0.04). The fact that the CNN recognition of the optic chiasm fails for chiasm abnormalities in PWA underlines the fundamental differences in their spatial features. This finding provides proof of concept for a novel deep-learning-based diagnostics approach of chiasmal misrouting from T1w images, as well as further analyses on chiasmal misrouting and their impact on the structure and function of the visual system.
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spelling pubmed-85734102021-11-09 Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images Puzniak, Robert J. Prabhakaran, Gokulraj T. Hoffmann, Michael B. Front Neurosci Neuroscience Convolutional neural network (CNN) models are of great promise to aid the segmentation and analysis of brain structures. Here, we tested whether CNN trained to segment normal optic chiasms from the T1w magnetic resonance imaging (MRI) image can be also applied to abnormal chiasms, specifically with optic nerve misrouting as typical for human albinism. We performed supervised training of the CNN on the T1w images of control participants (n = 1049) from the Human Connectome Project (HCP) repository and automatically generated algorithm-based optic chiasm masks. The trained CNN was subsequently tested on data of persons with albinism (PWA; n = 9) and controls (n = 8) from the CHIASM repository. The quality of outcome segmentation was assessed via the comparison to manually defined optic chiasm masks using the Dice similarity coefficient (DSC). The results revealed contrasting quality of masks obtained for control (mean DSC ± SEM = 0.75 ± 0.03) and PWA data (0.43 ± 0.8, few-corrected p = 0.04). The fact that the CNN recognition of the optic chiasm fails for chiasm abnormalities in PWA underlines the fundamental differences in their spatial features. This finding provides proof of concept for a novel deep-learning-based diagnostics approach of chiasmal misrouting from T1w images, as well as further analyses on chiasmal misrouting and their impact on the structure and function of the visual system. Frontiers Media S.A. 2021-10-25 /pmc/articles/PMC8573410/ /pubmed/34759795 http://dx.doi.org/10.3389/fnins.2021.755785 Text en Copyright © 2021 Puzniak, Prabhakaran and Hoffmann. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Puzniak, Robert J.
Prabhakaran, Gokulraj T.
Hoffmann, Michael B.
Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images
title Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images
title_full Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images
title_fullStr Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images
title_full_unstemmed Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images
title_short Deep Learning-Based Detection of Malformed Optic Chiasms From MRI Images
title_sort deep learning-based detection of malformed optic chiasms from mri images
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573410/
https://www.ncbi.nlm.nih.gov/pubmed/34759795
http://dx.doi.org/10.3389/fnins.2021.755785
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AT hoffmannmichaelb deeplearningbaseddetectionofmalformedopticchiasmsfrommriimages