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Delineating critical structures of the brain is required for advanced radiotherapy technologies to determine whether the dose from the proposed treatment will impair the functionality of those structures. Employing an automatic segmentation computer module in the radiation oncology treatment planning process has the potential to significantly increase the efficiency, cost-effectiveness, and, ultimately, clinical outcome of patients undergoing radiation therapy. Atlas-based segmentation has shown to be a suitable tool for the segmentation of large structures such as the brainstem or the cerebellum. However, smaller structures such as the optic nerves are more difficult to segment. In this work, we present a novel approach to automatically segment the optic nerves, which is based on Support Vector Machines (SVM). Compared to state of the art methods, the presented method obtained a better performance in regards to accuracy, robustness and processing time, being a suitable trade-off between these three factors.
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Dolz et al. (2015) studied this question.
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