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Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients
Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-base...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406204/ https://www.ncbi.nlm.nih.gov/pubmed/37555156 http://dx.doi.org/10.3389/fnimg.2022.832512 |
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author | Ranjbar, Sara Singleton, Kyle W. Curtin, Lee Rickertsen, Cassandra R. Paulson, Lisa E. Hu, Leland S. Mitchell, Joseph Ross Swanson, Kristin R. |
author_facet | Ranjbar, Sara Singleton, Kyle W. Curtin, Lee Rickertsen, Cassandra R. Paulson, Lisa E. Hu, Leland S. Mitchell, Joseph Ross Swanson, Kristin R. |
author_sort | Ranjbar, Sara |
collection | PubMed |
description | Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board–approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance. |
format | Online Article Text |
id | pubmed-10406204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104062042023-08-08 Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients Ranjbar, Sara Singleton, Kyle W. Curtin, Lee Rickertsen, Cassandra R. Paulson, Lisa E. Hu, Leland S. Mitchell, Joseph Ross Swanson, Kristin R. Front Neuroimaging Neuroimaging Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board–approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance. Frontiers Media S.A. 2022-04-25 /pmc/articles/PMC10406204/ /pubmed/37555156 http://dx.doi.org/10.3389/fnimg.2022.832512 Text en Copyright © 2022 Ranjbar, Singleton, Curtin, Rickertsen, Paulson, Hu, Mitchell and Swanson. 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 | Neuroimaging Ranjbar, Sara Singleton, Kyle W. Curtin, Lee Rickertsen, Cassandra R. Paulson, Lisa E. Hu, Leland S. Mitchell, Joseph Ross Swanson, Kristin R. Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients |
title | Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients |
title_full | Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients |
title_fullStr | Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients |
title_full_unstemmed | Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients |
title_short | Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients |
title_sort | weakly supervised skull stripping of magnetic resonance imaging of brain tumor patients |
topic | Neuroimaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406204/ https://www.ncbi.nlm.nih.gov/pubmed/37555156 http://dx.doi.org/10.3389/fnimg.2022.832512 |
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