An ensemble of 2D convolutional neural networks for tumor segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9127, Issue , 2015, Pages 201-211

  Lyksborg, Mark ^a   Puonti, Oula ^a   Agn, Mikael ^a   Larsen, Rasmus ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Cellular automaton; Convolutional neural network; Ensemble classification; Tumor segmentation

Indexed keywords

CELLULAR AUTOMATA; CONVOLUTION; IMAGE ANALYSIS; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS; RADIOTHERAPY; TUMORS;

BRAIN TUMOR SEGMENTATION; CONVOLUTIONAL NEURAL NETWORK; ENSEMBLE CLASSIFICATION; ORTHOGONAL PLANE; RADIOTHERAPY TREATMENT; SEGMENTATION ACCURACY; TUMOR SEGMENTATION; VOLUMETRIC SEGMENTATIONS;

IMAGE SEGMENTATION;

EID: 84947921127     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-19665-7_17     Document Type: Conference Paper

References (18)

1. Bergstra, J., et al.: Theano: a CPU and GPU math expression compiler. In: Python for Scientific Computing Conference (SciPy) (2010)
2. Cordier, N., Menze, B., Delingette, H., Ayache, N.: Patch-based segmentation of brain tissues. In: MICCAI-BraTS (Challenge on Multimodal Brain Tumor Segmentation), pp. 6–17 (2013)
3. Davy, A., Havaei, M., Warde-Farley, D., Biard, A., Tran, L., Jodoin, P.M., Courville, A., Larochelle, H., Pal, C., Bengio, Y.: Brain tumor segmentation with deep neural networks. In: MICCAI-BraTS, pp. 1–5 (2014)
4. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology (1945)
5. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. CoRR (2012)
6. Jakab, A.: Segmenting brain tumors with the slicer 3d software. Tech. rep., University of Debrecen/ETH Z¨urich (2012)
7. Kleesiek, J., Biller, A., Urban, G., Kothe, U., Bendszus, M., Hamprecht, F.: Ilastik for multi-modal brain tumor segmentation. In: MICCAI-BraTS, pp. 12–17 (2014)
8. Kwon, D., Akbari, H., Da, X., Gaonkar, B., Davatzikos, C.: Multimodal brain tumor image segmentation using glistr. In: MICCAI-BraTS, pp. 18–19 (2014)
9. Menze, B., Geremia, E., Ayache, N., Szekely, G.: Segmenting glioma in multimodal images using a generative-discriminative model for brain lesion segmentation. In: MICCAI-BraTS, pp. 56–63 (2012)
10. Menze, B., Leemput, K.V., Lashkar, D., Weber, M., Ayache, N., Golland, P.: Segmenting glioma in multi-modal images using a generative model for brain lesion segmentation, pp. 49–55 (2012)
11. Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Transactions on Medical Imaging (2014)
12. Reza, S., Iftekharuddin, K.: Improved brain tumor tissue segmentation using texture features. In: MICCAI-BraTS, pp. 27–30 (2014)
13. Sutskever, I., Martens, J., Dahl, G.E., Hinton, G.E.: On the importance of initialization and momentum in deep learning. In: 30th International Conference on Machine Learning (ICML 2013), vol. 28, pp. 1139–1147, May 2013
14. Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: Improved N3 Bias Correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
15. Tustison, N., Wintermark, M., Durst, C., Avants, B.: Ants and arboles. In: MICCAI-BraTS, pp. 47–50 (2013)
16. Urban, G., Bendszus, M., Hamprecht, F.A., Kleesiek, J.: Multi-modal brain tumor segmentation using deep convolutional neural networks. In: MICCAI-BraTSs, pp. 31–35 (2014)
17. Vezhnevets, V., Konouchine, V.: GrowCut - interactive multi-label n-d image segmentation by cellular automata. In: Proceedings of Graphicon (2005)
18. Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks. In: MICCAI-BraTS, pp. 36–39 (2014)