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A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images
BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, and differentiating between these two forms is manda...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196418/ https://www.ncbi.nlm.nih.gov/pubmed/30348148 http://dx.doi.org/10.1186/s12917-018-1638-2 |
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author | Banzato, Tommaso Bernardini, Marco Cherubini, Giunio B. Zotti, Alessandro |
author_facet | Banzato, Tommaso Bernardini, Marco Cherubini, Giunio B. Zotti, Alessandro |
author_sort | Banzato, Tommaso |
collection | PubMed |
description | BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, and differentiating between these two forms is mandatory in choosing the correct therapy. The aims of the present study are: 1) to determine the accuracy of a deep convolutional neural network (CNN, GoogleNet) in discriminating between meningiomas and gliomas in pre- and post-contrast T1 images and T2 images; 2) to develop an image classifier, based on the combination of CNN and MRI sequence displaying the highest accuracy, to predict whether a lesion is a meningioma or a glioma. RESULTS: Eighty cases with a final diagnosis of meningioma (n = 56) and glioma (n = 24) from two different institutions were included in the study. A pre-trained CNN was retrained on our data through a process called transfer learning. To evaluate CNN accuracy in the different imaging sequences, the dataset was divided into a training, a validation and a test set. The accuracy of the CNN was calculated on the test set. The combination between post-contrast T1 images and CNN was chosen in developing the image classifier (trCNN). Ten images from challenging cases were excluded from the database in order to test trCNN accuracy; the trCNN was trained on the remainder of the dataset of post-contrast T1 images, and correctly classified all the selected images. To compensate for the imbalance between meningiomas and gliomas in the dataset, the Matthews correlation coefficient (MCC) was also calculated. The trCNN showed an accuracy of 94% (MCC = 0.88) on post-contrast T1 images, 91% (MCC = 0.81) on pre-contrast T1-images and 90% (MCC = 0.8) on T2 images. CONCLUSIONS: The developed trCNN could be a reliable tool in distinguishing between different meningiomas and gliomas from MR images. |
format | Online Article Text |
id | pubmed-6196418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61964182018-10-30 A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images Banzato, Tommaso Bernardini, Marco Cherubini, Giunio B. Zotti, Alessandro BMC Vet Res Methodology Article BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, and differentiating between these two forms is mandatory in choosing the correct therapy. The aims of the present study are: 1) to determine the accuracy of a deep convolutional neural network (CNN, GoogleNet) in discriminating between meningiomas and gliomas in pre- and post-contrast T1 images and T2 images; 2) to develop an image classifier, based on the combination of CNN and MRI sequence displaying the highest accuracy, to predict whether a lesion is a meningioma or a glioma. RESULTS: Eighty cases with a final diagnosis of meningioma (n = 56) and glioma (n = 24) from two different institutions were included in the study. A pre-trained CNN was retrained on our data through a process called transfer learning. To evaluate CNN accuracy in the different imaging sequences, the dataset was divided into a training, a validation and a test set. The accuracy of the CNN was calculated on the test set. The combination between post-contrast T1 images and CNN was chosen in developing the image classifier (trCNN). Ten images from challenging cases were excluded from the database in order to test trCNN accuracy; the trCNN was trained on the remainder of the dataset of post-contrast T1 images, and correctly classified all the selected images. To compensate for the imbalance between meningiomas and gliomas in the dataset, the Matthews correlation coefficient (MCC) was also calculated. The trCNN showed an accuracy of 94% (MCC = 0.88) on post-contrast T1 images, 91% (MCC = 0.81) on pre-contrast T1-images and 90% (MCC = 0.8) on T2 images. CONCLUSIONS: The developed trCNN could be a reliable tool in distinguishing between different meningiomas and gliomas from MR images. BioMed Central 2018-10-22 /pmc/articles/PMC6196418/ /pubmed/30348148 http://dx.doi.org/10.1186/s12917-018-1638-2 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Methodology Article Banzato, Tommaso Bernardini, Marco Cherubini, Giunio B. Zotti, Alessandro A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images |
title | A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images |
title_full | A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images |
title_fullStr | A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images |
title_full_unstemmed | A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images |
title_short | A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images |
title_sort | methodological approach for deep learning to distinguish between meningiomas and gliomas on canine mr-images |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6196418/ https://www.ncbi.nlm.nih.gov/pubmed/30348148 http://dx.doi.org/10.1186/s12917-018-1638-2 |
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