Browsing by Author "Lin, Chung-Jung"

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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
(2019-01-01)
Peng, Syu-Jyun
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Lee, Cheng-chia
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Wu, Hsiu-Mei
;
Lin, Chung-Jung
;
Shiau, Cheng-Ying
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.
Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm
(2019-05-01)
Lee, Cheng-Chia
;
Yang, Huai-Che
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Lin, Chung-Jung
;
Chen, Ching-Jen
;
Wu, Hsiu-Mei
OBJECTIVE: To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidal component identification and quantification using an unsupervised machine learning algorithm and to evaluate the association between intervening nidal brain parenchyma and radiationinduced changes (RICs) after stereotactic radiosurgery. METHODS: Fully automated segmentation via unsupervised classification with fuzzy c-means clustering was used to analyze the AVM nidus on T2-weighted magnetic resonance imaging studies. The proportions of vasculature, brain parenchyma, and cerebrospinal fluid were quantified. These were compared with the results from manual segmentation. The association between the brain parenchyma component and RIC development was assessed. RESULTS: The proposed algorithm was applied to 39 unruptured AVMs in 39 patients (17 female and 22 male patients), with a median age of 27 years. The median proportion of the constituents was as follows: vasculature, 31.3%; brain parenchyma, 48.4%; and cerebrospinal fluid, 16.8%. RICs were identified in 17 of the 39 patients (43.6%). Compared with manual segmentation, the automated algorithm was able to achieve a Dice similarity index of 79.5% (sensitivity, 73.5%; specificity, 85.5%). RICs were associated with a greater proportion of intervening nidal brain parenchyma (52.0% vs. 45.3%; P = 0.015). Obliteration was not associated with greater proportions of nidal vasculature (36.0% vs. 31.2%; P = 0.152). CONCLUSIONS: The automated segmentation algorithm was able to achieve classification of the AVM nidus components with relative accuracy. Greater proportions of intervening nidal brain parenchyma were associated with RICs.