Improving Boundary Classification for Brain Tumor Segmentation and Longitudinal Disease Progression

  • Ramandeep S. Randhawa
  • Ankit Modi
  • Parag Jain
  • Prashant Warier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)

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.

Keywords

Deep neural networks Segmentation Loss functions Glioblastoma 

References

  1. 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(10), 1993–2024 (2015)CrossRefGoogle Scholar
  2. 2.
    Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., Larochelle, H.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18–31 (2016)CrossRefGoogle Scholar
  3. 3.
    Havaei, M., Dutil, F., Pal, C., Larochelle, H., Jodoin, P.-M.: A convolutional neural network approach to brain tumor segmentation. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 195–208. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_17 CrossRefGoogle Scholar
  4. 4.
    Pereira, S., Pinto, A., Alves, V., Silva, C.A.: Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds.) BrainLes 2015. LNCS, vol. 9556, pp. 131–143. Springer, Cham (2016). doi: 10.1007/978-3-319-30858-6_12 CrossRefGoogle Scholar
  5. 5.
    Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J.: Multi-modal brain tumor segmentation using deep convolutional neural networks. In: proceedings of the BRATS-MICCAI (2014)Google Scholar
  6. 6.
    Zikic, D., Ioannou, Y., Brown, M., Criminisi, A.: Segmentation of brain tumor tissues with convolutional neural networks. In: Proceedings of the MICCAI-BRATS, pp. 36–39 (2014)Google Scholar
  7. 7.
    Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). doi: 10.1007/978-3-319-24574-4_28 CrossRefGoogle Scholar
  8. 8.
    Rao, V., Sarabi, M.S., Jaiswal, A.: Brain tumor segmentation with deep learning. In: MICCAI BraTS (Brain Tumor Segmentation) Challenge, pp. 31–35 (2014)Google Scholar
  9. 9.
    Shin, H.-C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D.J., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. CoRR abs/1602.03409 (2016). http://dblp.uni-trier.de/rec/bib/journals/corr/ShinRGLXNYMS16

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ramandeep S. Randhawa
    • 1
  • Ankit Modi
    • 2
  • Parag Jain
    • 3
  • Prashant Warier
    • 2
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.Fractal AnalyticsMumbaiIndia
  3. 3.Dhristi Inc.Palo AltoUSA

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