Abstract
Planning a deep brain stimulation surgery in Parkinson disease is a critical task because the medical team needs to accurately locate the basal ganglia area (i.e. sub-thalamus) in a magnetic resonance image study. This paper proposes a new method for shape prior information based on the Chan-Vese model and Bayesian shape models for brain structure segmentation on magnetic resonance images. The method allows to initialize efficiently a given shape by fitting an active contour (Chan-Vese model), and then robustly fits a brain structure, performing a Bayesian shape fitting. The experimental results show that the proposed model can effectively segment a brain structure. Also, the proposed model, provides a fast segmentation which improves the computational cost compared with common segmentation techniques such as active shape models.
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García, H.F., Álvarez, M.A., Orozco, Á. (2014). Bayesian Shape Models with Shape Priors for MRI Brain Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_82
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DOI: https://doi.org/10.1007/978-3-319-14364-4_82
Publisher Name: Springer, Cham
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