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21 March 2016 Automatic brain tumor segmentation with a fast Mumford-Shah algorithm
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Abstract
We propose a fully-automatic method for brain tumor segmentation that does not require any training phase. Our approach is based on a sequence of segmentations using the Mumford-Shah cartoon model with varying parameters. In order to come up with a very fast implementation, we extend the recent primal-dual algorithm of Strekalovskiy et al. (2014) from the 2D to the medically relevant 3D setting. Moreover, we suggest a new confidence refinement and show that it can increase the precision of our segmentations substantially. Our method is evaluated on 188 data sets with high-grade gliomas and 25 with low-grade gliomas from the BraTS14 database. Within a computation time of only three minutes, we achieve Dice scores that are comparable to state-of-the-art methods.
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Sabine Müller, Joachim Weickert, and Norbert Graf "Automatic brain tumor segmentation with a fast Mumford-Shah algorithm", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842S (21 March 2016); https://doi.org/10.1117/12.2214552
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