標題: 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
作者: 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
電子工程學系及電子研究所
生醫電子轉譯研究中心
Department of Electronics Engineering and Institute of Electronics
Biomedical Electronics Translational Research Center
關鍵字: Cerebral Arterio-Venous Malformation;Gamma knife radiosurgery;Radiotherapy;Radiation side-effect;Fuzzy c-means clustering
公開日期: 1-一月-2019
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.
URI: http://dx.doi.org/10.1016/j.nicl.2018.11.018
http://hdl.handle.net/11536/148953
ISSN: 2213-1582
DOI: 10.1016/j.nicl.2018.11.018
期刊: NEUROIMAGE-CLINICAL
Volume: 21
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