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dc.contributor.authorPeng, Syu-Jyunen_US
dc.contributor.authorLee, Cheng-chiaen_US
dc.contributor.authorWu, Hsiu-Meien_US
dc.contributor.authorLin, Chung-Jungen_US
dc.contributor.authorShiau, Cheng-Yingen_US
dc.contributor.authorGuo, Wan-Yuoen_US
dc.contributor.authorPan, David Hung-Chien_US
dc.contributor.authorLiu, Kang-Duen_US
dc.contributor.authorChung, Wen-Yuhen_US
dc.contributor.authorYang, Huai-Cheen_US
dc.description.abstractBackground: 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.en_US
dc.subjectCerebral Arterio-Venous Malformationen_US
dc.subjectGamma knife radiosurgeryen_US
dc.subjectRadiation side-effecten_US
dc.subjectFuzzy c-means clusteringen_US
dc.titleFully automated tissue segmentation of the prescription isodose region delineated through the Gamma knife plan for cerebral arteriovenous malformation (AVM) using fuzzy C-means (FCM) clusteringen_US
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.contributor.departmentBiomedical Electronics Translational Research Centeren_US
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