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Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla MRI
Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies....
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127160/ https://www.ncbi.nlm.nih.gov/pubmed/33738898 http://dx.doi.org/10.1002/hbm.25409 |
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author | Solomon, Oren Palnitkar, Tara Patriat, Re'mi Braun, Henry Aman, Joshua Park, Michael C. Vitek, Jerrold Sapiro, Guillermo Harel, Noam |
author_facet | Solomon, Oren Palnitkar, Tara Patriat, Re'mi Braun, Henry Aman, Joshua Park, Michael C. Vitek, Jerrold Sapiro, Guillermo Harel, Noam |
author_sort | Solomon, Oren |
collection | PubMed |
description | Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep‐learning based neural network, which we term GP‐net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP‐net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP‐net performs 3D inference in a patient‐specific manner, alleviating the need for atlas‐based segmentation. GP‐net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state‐of‐the‐art atlas‐based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP‐net. |
format | Online Article Text |
id | pubmed-8127160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81271602021-05-21 Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla MRI Solomon, Oren Palnitkar, Tara Patriat, Re'mi Braun, Henry Aman, Joshua Park, Michael C. Vitek, Jerrold Sapiro, Guillermo Harel, Noam Hum Brain Mapp Research Articles Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep‐learning based neural network, which we term GP‐net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP‐net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP‐net performs 3D inference in a patient‐specific manner, alleviating the need for atlas‐based segmentation. GP‐net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state‐of‐the‐art atlas‐based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP‐net. John Wiley & Sons, Inc. 2021-03-18 /pmc/articles/PMC8127160/ /pubmed/33738898 http://dx.doi.org/10.1002/hbm.25409 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Solomon, Oren Palnitkar, Tara Patriat, Re'mi Braun, Henry Aman, Joshua Park, Michael C. Vitek, Jerrold Sapiro, Guillermo Harel, Noam Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla MRI |
title |
Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla
MRI
|
title_full |
Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla
MRI
|
title_fullStr |
Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla
MRI
|
title_full_unstemmed |
Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla
MRI
|
title_short |
Deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 Tesla
MRI
|
title_sort | deep‐learning based fully automatic segmentation of the globus pallidus interna and externa using ultra‐high 7 tesla
mri |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127160/ https://www.ncbi.nlm.nih.gov/pubmed/33738898 http://dx.doi.org/10.1002/hbm.25409 |
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