Abstract
We present a method for automatic segmentation of high-grade gliomas and their subregions from multi-channel MR images. Besides segmenting the gross tumor, we also differentiate between active cells, necrotic core, and edema. Our discriminative approach is based on decision forests using context-aware spatial features, and integrates a generative model of tissue appearance, by using the probabilities obtained by tissue-specific Gaussian mixture models as additional input for the forest. Our method classifies the individual tissue types simultaneously, which has the potential to simplify the classification task. The approach is computationally efficient and of low model complexity. The validation is performed on a labeled database of 40 multi-channel MR images, including DTI. We assess the effects of using DTI, and varying the amount of training data. Our segmentation results are highly accurate, and compare favorably to the state of the art.
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Zikic, D. et al. (2012). Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33454-2_46
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DOI: https://doi.org/10.1007/978-3-642-33454-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33453-5
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Keywords
- Gaussian Mixture Model
- Necrotic Core
- Magnetic Resonance Spectroscopic Image
- Discriminative Approach
- Tumor Growth Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
