Fully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering

Syu Jyun Peng, Cheng chia Lee, Hsiu Mei Wu, Chung Jung Lin, Cheng Ying Shiau, Wan Yuo Guo, David Hung Chi Pan, Kang Du Liu, Wen Yuh Chung, Huai Che Yang

研究成果: 雜誌貢獻文章

1 引文 (Scopus)

摘要

Background: Gamma knife radiosurgery (GKRS) is a common treatment for cerebral arterio-venous malformations (AVMs), particularly in cases where the malformation is deep-seated, large, or in eloquent areas of the brain. Unfortunately, these procedures can result in radiation injury to brain parenchyma. The fact that every AVM is unique in its vascular morphology makes it nearly impossible to exclude brain parenchyma from isodose radiation exposure during the formulation of a GKRS plan. Calculating the percentages of the various forms of tissue exposed to specific doses of radiation is crucial to understanding the clinical responses and causes of brain parenchyma injury following GKRS for AVM. Methods: In this study, we developed a fully automated algorithm using unsupervised classification via fuzzy c-means clustering for the analysis of T2 weighted images used in a Gamma knife plan. This algorithm is able to calculate the percentages of nidus, brain tissue, and cerebrospinal fluid (CSF) within the prescription isodose radiation exposure region. Results: The proposed algorithm was used to assess the treatment plan of 25 patients with AVM who had undergone GKRS. The Dice similarity index (SI) was used to determine the degree of agreement between the results obtained using the algorithm and a visually guided manual method (the gold standard) performed by an experienced neurosurgeon. In the nidus, the SI was (74.86 ± 1.30%) (mean ± standard deviation), the sensitivity was (83.05 ± 11.91)%, and the specificity was (86.73 ± 10.31)%. In brain tissue, the SI was (79.50 ± 6.01)%, the sensitivity was (73.05 ± 9.77)%, and the specificity was (85.53 ± 7.13)%. In the CSF, the SI was (69.57 ± 15.26)%, the sensitivity was (89.86 ± 5.87)%, and the specificity was (92.36 ± 4.35)%. Conclusions: The proposed clustering algorithm provides precise percentages of the various types of tissue within the prescription isodose region in the T2 weighted images used in the GKRS plan for AVM. Our results shed light on the causes of brain radiation injury after GKRS for AVM. In the future, this system could be used to improve outcomes and avoid complications associated with GKRS treatment.
原文英語
文章編號101608
期刊NeuroImage: Clinical
21
DOIs
出版狀態已發佈 - 一月 1 2019
對外發佈Yes

指紋

Intracranial Arteriovenous Malformations
Radiosurgery
Prescriptions
Cluster Analysis
Arteriovenous Malformations
Brain
Radiation Injuries
Brain Injuries
Cerebrospinal Fluid
Blood Vessels
Therapeutics
Radiation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

引用此文

Fully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering. / Peng, Syu Jyun; Lee, Cheng chia; Wu, Hsiu Mei; Lin, Chung Jung; Shiau, Cheng Ying; Guo, Wan Yuo; Pan, David Hung Chi; Liu, Kang Du; Chung, Wen Yuh; Yang, Huai Che.

於: NeuroImage: Clinical, 卷 21, 101608, 01.01.2019.

研究成果: 雜誌貢獻文章

Peng, Syu Jyun ; Lee, Cheng chia ; Wu, Hsiu Mei ; Lin, Chung Jung ; Shiau, Cheng Ying ; Guo, Wan Yuo ; Pan, David Hung Chi ; Liu, Kang Du ; Chung, Wen Yuh ; Yang, Huai Che. / Fully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering. 於: NeuroImage: Clinical. 2019 ; 卷 21.
@article{9817b657dea041768cd83ce33319cada,
title = "Fully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering",
abstract = "Background: Gamma knife radiosurgery (GKRS) is a common treatment for cerebral arterio-venous malformations (AVMs), particularly in cases where the malformation is deep-seated, large, or in eloquent areas of the brain. Unfortunately, these procedures can result in radiation injury to brain parenchyma. The fact that every AVM is unique in its vascular morphology makes it nearly impossible to exclude brain parenchyma from isodose radiation exposure during the formulation of a GKRS plan. Calculating the percentages of the various forms of tissue exposed to specific doses of radiation is crucial to understanding the clinical responses and causes of brain parenchyma injury following GKRS for AVM. Methods: In this study, we developed a fully automated algorithm using unsupervised classification via fuzzy c-means clustering for the analysis of T2 weighted images used in a Gamma knife plan. This algorithm is able to calculate the percentages of nidus, brain tissue, and cerebrospinal fluid (CSF) within the prescription isodose radiation exposure region. Results: The proposed algorithm was used to assess the treatment plan of 25 patients with AVM who had undergone GKRS. The Dice similarity index (SI) was used to determine the degree of agreement between the results obtained using the algorithm and a visually guided manual method (the gold standard) performed by an experienced neurosurgeon. In the nidus, the SI was (74.86 ± 1.30{\%}) (mean ± standard deviation), the sensitivity was (83.05 ± 11.91){\%}, and the specificity was (86.73 ± 10.31){\%}. In brain tissue, the SI was (79.50 ± 6.01){\%}, the sensitivity was (73.05 ± 9.77){\%}, and the specificity was (85.53 ± 7.13){\%}. In the CSF, the SI was (69.57 ± 15.26){\%}, the sensitivity was (89.86 ± 5.87){\%}, and the specificity was (92.36 ± 4.35){\%}. Conclusions: The proposed clustering algorithm provides precise percentages of the various types of tissue within the prescription isodose region in the T2 weighted images used in the GKRS plan for AVM. Our results shed light on the causes of brain radiation injury after GKRS for AVM. In the future, this system could be used to improve outcomes and avoid complications associated with GKRS treatment.",
keywords = "Cerebral Arterio-Venous Malformation, Fuzzy c-means clustering, Gamma knife radiosurgery, Radiation side-effect, Radiotherapy",
author = "Peng, {Syu Jyun} and Lee, {Cheng chia} and Wu, {Hsiu Mei} and Lin, {Chung Jung} and Shiau, {Cheng Ying} and Guo, {Wan Yuo} and Pan, {David Hung Chi} and Liu, {Kang Du} and Chung, {Wen Yuh} and Yang, {Huai Che}",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.nicl.2018.11.018",
language = "English",
volume = "21",
journal = "NeuroImage: Clinical",
issn = "2213-1582",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Fully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clustering

AU - Peng, Syu Jyun

AU - Lee, Cheng chia

AU - Wu, Hsiu Mei

AU - Lin, Chung Jung

AU - Shiau, Cheng Ying

AU - Guo, Wan Yuo

AU - Pan, David Hung Chi

AU - Liu, Kang Du

AU - Chung, Wen Yuh

AU - Yang, Huai Che

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Background: Gamma knife radiosurgery (GKRS) is a common treatment for cerebral arterio-venous malformations (AVMs), particularly in cases where the malformation is deep-seated, large, or in eloquent areas of the brain. Unfortunately, these procedures can result in radiation injury to brain parenchyma. The fact that every AVM is unique in its vascular morphology makes it nearly impossible to exclude brain parenchyma from isodose radiation exposure during the formulation of a GKRS plan. Calculating the percentages of the various forms of tissue exposed to specific doses of radiation is crucial to understanding the clinical responses and causes of brain parenchyma injury following GKRS for AVM. Methods: In this study, we developed a fully automated algorithm using unsupervised classification via fuzzy c-means clustering for the analysis of T2 weighted images used in a Gamma knife plan. This algorithm is able to calculate the percentages of nidus, brain tissue, and cerebrospinal fluid (CSF) within the prescription isodose radiation exposure region. Results: The proposed algorithm was used to assess the treatment plan of 25 patients with AVM who had undergone GKRS. The Dice similarity index (SI) was used to determine the degree of agreement between the results obtained using the algorithm and a visually guided manual method (the gold standard) performed by an experienced neurosurgeon. In the nidus, the SI was (74.86 ± 1.30%) (mean ± standard deviation), the sensitivity was (83.05 ± 11.91)%, and the specificity was (86.73 ± 10.31)%. In brain tissue, the SI was (79.50 ± 6.01)%, the sensitivity was (73.05 ± 9.77)%, and the specificity was (85.53 ± 7.13)%. In the CSF, the SI was (69.57 ± 15.26)%, the sensitivity was (89.86 ± 5.87)%, and the specificity was (92.36 ± 4.35)%. Conclusions: The proposed clustering algorithm provides precise percentages of the various types of tissue within the prescription isodose region in the T2 weighted images used in the GKRS plan for AVM. Our results shed light on the causes of brain radiation injury after GKRS for AVM. In the future, this system could be used to improve outcomes and avoid complications associated with GKRS treatment.

AB - Background: Gamma knife radiosurgery (GKRS) is a common treatment for cerebral arterio-venous malformations (AVMs), particularly in cases where the malformation is deep-seated, large, or in eloquent areas of the brain. Unfortunately, these procedures can result in radiation injury to brain parenchyma. The fact that every AVM is unique in its vascular morphology makes it nearly impossible to exclude brain parenchyma from isodose radiation exposure during the formulation of a GKRS plan. Calculating the percentages of the various forms of tissue exposed to specific doses of radiation is crucial to understanding the clinical responses and causes of brain parenchyma injury following GKRS for AVM. Methods: In this study, we developed a fully automated algorithm using unsupervised classification via fuzzy c-means clustering for the analysis of T2 weighted images used in a Gamma knife plan. This algorithm is able to calculate the percentages of nidus, brain tissue, and cerebrospinal fluid (CSF) within the prescription isodose radiation exposure region. Results: The proposed algorithm was used to assess the treatment plan of 25 patients with AVM who had undergone GKRS. The Dice similarity index (SI) was used to determine the degree of agreement between the results obtained using the algorithm and a visually guided manual method (the gold standard) performed by an experienced neurosurgeon. In the nidus, the SI was (74.86 ± 1.30%) (mean ± standard deviation), the sensitivity was (83.05 ± 11.91)%, and the specificity was (86.73 ± 10.31)%. In brain tissue, the SI was (79.50 ± 6.01)%, the sensitivity was (73.05 ± 9.77)%, and the specificity was (85.53 ± 7.13)%. In the CSF, the SI was (69.57 ± 15.26)%, the sensitivity was (89.86 ± 5.87)%, and the specificity was (92.36 ± 4.35)%. Conclusions: The proposed clustering algorithm provides precise percentages of the various types of tissue within the prescription isodose region in the T2 weighted images used in the GKRS plan for AVM. Our results shed light on the causes of brain radiation injury after GKRS for AVM. In the future, this system could be used to improve outcomes and avoid complications associated with GKRS treatment.

KW - Cerebral Arterio-Venous Malformation

KW - Fuzzy c-means clustering

KW - Gamma knife radiosurgery

KW - Radiation side-effect

KW - Radiotherapy

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DO - 10.1016/j.nicl.2018.11.018

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