Abstract
In this work, we investigated the potential of a recently proposed parameter learning algorithm for Conditional Random Fields (CRFs). Parameters of a pairwise CRF are estimated via a stochastic subgradient descent of a max-margin learning problem. We compared the performance of our brain tumor segmentation method using parameter learning to a version using hand-tuned parameters. Preliminary results on a subset of the BRATS2015 training set show that parameter learning leads to comparable or even improved performance. In addition, we also performed experiments to study the impact of the composition of training data on the final segmentation performance. We found that models trained on mixed data sets achieve reasonable performance compared to models trained on stratified data.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Fast-PD requires \(B_{i,j}\) to define a semi-metric.
References
Wen, P.Y., Macdonald, D.R., Reardon, D.A., Cloughesy, T.F., Sorensen, A.G., et al.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28(11), 1963–1972 (2010)
Reuter, M., Gerstner, E.R., Rapalino, O., et al.: Impact of MRI head placement on glioma response assessment. J. Neuro-Oncol. 118, 123–129 (2014)
Kanaly, C.W., Ding, D., Mehta, A.I., Waller, A.F., Crocker, I., Desjardins, A., Reardon, D.A., Friedman, A.H., Bigner, D.D., Sampson, J.H.: A novel method for volumetric MRI response assessment of enhancing brain tumors. PLoS ONE 6(1), e16031 (2011)
Porz, N., Bauer, S., Pica, A., Schucht, P., Beck, J., Verma, R.K., Slotboom, J., Reyes, M., Wiest, R.: Multi-modal glioblastoma segmentation: man versus machine. PLoS ONE 9(5), e96873 (2014)
Velazquez, E.R., Meier, R., Dunn Jr., W.D., Alexander, B., Bauer, S., Gutman, D.A., Reyes, M., Aerts, H.J.W.L.: Fully automatic GBM segmentation in the TCGA-GBM dataset : prognosis and correlation with VASARI features. Nat. Sci. Rep. 5, 16822 (2015)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). In: TMI (2014)
Lombaert, H., Zikic, D., Criminisi, A., Ayache, N.: Laplacian forests: semantic image segmentation by guided bagging. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 496–504. Springer, Heidelberg (2014)
Zhao, L., Wu, W., Corso, J.J.: Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 567–575. Springer, Heidelberg (2013)
Meier, R., Bauer, S., Slotboom, J., Wiest, R., Reyes, M.: Appearance-and context-sensitive features for brain tumor segmentation. In: MICCAI BRATS Challenge (2014)
Bauer, S., Nolte, L.-P., Reyes, M.: Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 354–361. Springer, Heidelberg (2011)
Lucchi, A., Marquez-Neila, P., Becker, C., Li, Y., Smith, K., Knott, G., Fua, P.: Learning Structured Models for Segmentation of 2D and 3D Imagery. In: IEEE TMI, p. 1, March 2014
Taskar, B., Guestrin, C., Koller, D.: Max margin Markov networks. Neural Inf. Process. Syst. (2003)
Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML (2004)
Nowozin, S., Lampert, C.H.: Structured learning and prediction in computer vision. Found. Trends Comput. Graph. Vis. 6(3–4), 185–365 (2010)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer, Heidelberg (2013)
Ben Taskar, V., Chatalbashev, D.K., Guestrin, C.: Learning structured prediction models: a large margin approach. In: ICML (2005)
Ratliff, N.D., Andrew Bagnell, J., Zinkevich, M.A.: (Online) subgradient methods for structured prediction. Artif. Intell. Stat. (2007)
Komodakis, N., Tziritas, G.: Approximate labeling via graph cuts based on linear programming. IEEE TPAMI 29(8), 1436–1453 (2007)
Acknowledgments
This project has received funding from the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement No. 600841.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Meier, R., Karamitsou, V., Habegger, S., Wiest, R., Reyes, M. (2016). Parameter Learning for CRF-Based Tissue Segmentation of Brain Tumors. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-30858-6_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30857-9
Online ISBN: 978-3-319-30858-6
eBook Packages: Computer ScienceComputer Science (R0)