In this paper, we investigate a framework for interactive brain tumor segmentation which, at its core, treats the problem of interactive brain tumor segmentation as a machine learning problem.
This method has an advantage over typical machine learning methods for this task where generalization is made across brains. The problem with these methods is that they need to deal with intensity bias correction and other MRI-specific noise. In this paper, we avoid these issues by approaching the problem as one of within brain generalization. Specifically, we propose a semi-automatic method that segments a brain tumor by training and generalizing within that brain only, based on some minimum user interaction.
We investigate how adding spatial feature coordinates (i.e., i, j, k) to the intensity features can significantly improve the performance of different classification methods such as SVM, kNN and random forests. This would only be possible within an interactive framework. We also investigate the use of a more appropriate kernel and the adaptation of hyper-parameters specifically for each brain.
As a result of these experiments, we obtain an interactive method whose results reported on the MICCAI-BRATS 2013 dataset are the second most accurate compared to published methods, while using significantly less memory and processing power than most state-of-the-art methods.
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Please note that the results mentioned in Table 5 are from methods competing in the BRATS 2013 challenge for which a static table is provided (https://www.virtualskeleton.ch/BRATS/StaticResults2013). Since then, other methods have been added to the score board but for which no reference is available.
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Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with human participants performed by any of the authors.
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Havaei, M., Larochelle, H., Poulin, P. et al. Within-brain classification for brain tumor segmentation. Int J CARS 11, 777–788 (2016). https://doi.org/10.1007/s11548-015-1311-1
- Within-brain generalization
- Machine learning
- Brain tumor segmentation
- Computer-aided detection