Abstract
This paper extends a previously published brain tumor segmentation method with a dense Conditional Random Field (CRF). Dense CRFs can overcome the shrinking bias inherent to many grid-structured CRFs. We focus on illustrating the impact of alleviating the shrinking bias on the performance of CRF-based brain tumor segmentation. The proposed segmentation method is evaluated using data from the MICCAI BRATS 2013 & 2015 data sets (up to 110 patient cases for testing) and compared to a baseline method using a grid-structured CRF. Improved segmentation performance for the complete and enhancing tumor was observed with respect to grid-structured CRFs.
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This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement Nº600841.
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Meier, R., Knecht, U., Wiest, R., Reyes, M. (2016). CRF-Based Brain Tumor Segmentation: Alleviating the Shrinking Bias. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2016. Lecture Notes in Computer Science(), vol 10154. Springer, Cham. https://doi.org/10.1007/978-3-319-55524-9_10
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