Abstract
In this paper we present a new algorithm for 3D medical image segmentation. The algorithm is fast, relatively simple to implement, and semi-automatic. It is based on minimizing a global energy defined from a learned non-parametric estimation of the statistics of the region to be segmented. Implementation details are discussed and source code is freely available as part of the 3D Slicer project. In addition, a new unified set of validation metrics is proposed. Results on artificial and real MRI images show that the algorithm performs well on large brain structures both in terms of accuracy and robustness to noise.
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© 2003 Springer-Verlag Berlin Heidelberg
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Pichon, E., Tannenbaum, A., Kikinis, R. (2003). A Statistically Based Surface Evolution Method for Medical Image Segmentation: Presentation and Validation. In: Ellis, R.E., Peters, T.M. (eds) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003. MICCAI 2003. Lecture Notes in Computer Science, vol 2879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39903-2_87
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DOI: https://doi.org/10.1007/978-3-540-39903-2_87
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