Abstract
Tracking the progression of brain tumors is a challenging task, due to the slow growth rate and the combination of different tumor components, such as cysts, enhancing patterns, edema and necrosis. In this paper, we propose a Deep Neural Network based architecture that does automatic segmentation of brain tumor, and focuses on improving accuracy at the edges of these different classes. We show that enhancing the loss function to give more weight to the edge pixels significantly improves the neural network’s accuracy at classifying the boundaries. In the BRATS 2016 challenge, our submission placed third on the task of predicting progression for the complete tumor region.
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References
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Randhawa, R.S., Modi, A., Jain, P., Warier, P. (2016). Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression. 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_7
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DOI: https://doi.org/10.1007/978-3-319-55524-9_7
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