Brain Tumor Segmentation Using a Fully Convolutional Neural Network with Conditional Random Fields
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Publisher
Springer International Publishing
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http://link.springer.com/content/pdf/10.1007/978-3-319-55524-9_8
Reference18 articles.
1. Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015)
2. Bauer, S., Wiest, R., Nolte, L.-P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58, 97–129 (2013)
3. Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., Maier-Hein, K.H.: DALSA: domain adaptation for supervised learning from sparsely annotated MR images. IEEE Trans. Med. Imaging 35, 184–196 (2016)
4. Prastawa, M., Bullitt, E., Ho, S., Gerig, G.: A brain tumor segmentation framework based on outlier detection. Med. Image Anal. 8, 275–283 (2004)
5. Cobzas, D., Birkbeck, N., Schmidt, M., Jagersand, M., Murtha, A.: 3D variational brain tumor segmentation using a high dimensional feature set. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
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