Abstract
Accurate ventricle segmentation is important for reliable automated infarct localization, detection of early ischemic changes, and localization of hemorrhages. The purpose of this study was to develop a robust and accurate ventricle segmentation method in image data of ischemic and hemorrhagic stroke patients. Early follow-up non-contrast CT image data of 35 patients with a clinical diagnosis of ischemic stroke or subarachnoid hemorrhage were collected. We proposed a ventricle segmentation method based on a combination of active contours and an atlas-based segmentation. Ground truth was obtained by manual delineation of the ventricles by 4 observers with corrections by 2 experienced radiologists. Accuracy of the automated method was evaluated by calculation of the intraclass correlation coefficients, Dice coefficients, and by Bland-Altman analysis. The intraclass correlation coefficient for the automated method compared with the reference standard was excellent (0.93). The Dice coefficients was 0.79 [IQR: 0.72–0.84]. Bland-Altman analysis showed a mean difference of 2 mL between the automatic and manual measurements, with broad limits of agreement ranging from −18 to 15 mL. The automated ventricle segmentation showed an excellent correlation and high accuracy compared to the manual reference measurement. This approach is suitable for reliable ventricle segmentation even in stroke patients with a severely deformed brain.
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Ferdian, E. et al. (2017). Automated Ventricular System Segmentation in CT Images of Deformed Brains Due to Ischemic and Subarachnoid Hemorrhagic Stroke. In: Cardoso, M., et al. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. RAMBO CMMI SWITCH 2017 2017 2017. Lecture Notes in Computer Science(), vol 10555. Springer, Cham. https://doi.org/10.1007/978-3-319-67564-0_15
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