Abstract
We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Available on: https://www.cbica.upenn.edu/sbia/software/braintumorviewer/.
- 2.
Available on: https://ipp.cbica.upenn.edu/.
References
Akbari, H., Macyszyn, L., Da, X., Wolf, R.L., Bilello, M., Verma, R., O’Rourke, D.M., Davatzikos, C.: Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. Radiology 273(2), 502–510 (2014)
Bakas, S., Chatzimichail, K., Hunter, G., Labbe, B., Sidhu, P.S., Makris, D.: Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model. Comput. Methods Biomech. Biomed. Eng.: Imaging Vis., 1–10 (2015). doi:10.1080/21681163.2015.1029642
Deeley, M.A., Chen, A., Datteri, R., Noble, J.H., Cmelak, A.J., Donnelly, E.F., Malcolm, A.W., Moretti, L., Jaboin, J., Niermann, K., Yang, E.S., Yu, D.S., Yei, F., Koyama, T., Ding, G.X., Dawant, B.M.: Comparison of manual and automatic segmentation methods for brain structures in the presence of space-occupying lesions: a multi-expert study. Phy. Med. Biol. 56(14), 4557–4577 (2011)
Deschamps, T., Cohen, L.D.: Fast extraction of minimal paths in 3D images and applications to virtual endoscopy. Med. Image Anal. 5(4), 281–299 (2001)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)
Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)
Gaonkar, B., Macyszyn, L., Bilello, M., Sadaghiani, M.S., Akbari, H., Attiah, M.A., Ali, Z.S., Da, X., Zhan, Y., O’Rourke, D., Grady, S.M., Davatzikos, C.: Automated tumor volumetry using computer-aided image segmentation. Acad. Radiol. 22(5), 653–661 (2015)
Gooya, A., Biros, G., Davatzikos, C.: Deformable registration of glioma images using EM algorithm and diffusion reaction modeling. IEEE Trans. Med. Imaging 30(2), 375–390 (2011)
Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint segmentation and deformable registration of brain scans guided by a tumor growth model. Med. Image Comput. Comput.-Assist. Interv. 14(2), 532–540 (2011)
Gooya, A., Pohl, K.M., Bilello, M., Cirillo, L., Biros, G., Melhem, E.R., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31(10), 1941–1954 (2012)
Hogea, C., Davatzikos, C., Biros, G.: An image-driven parameter estimation problem for a reaction diffusion glioma growth model with mass effects. J. Math. Biol. 56(6), 793–825 (2008)
Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013)
Kwon, D., Akbari, H., Da, X., Gaonkar, B., Davatzikos, C.: Multimodal brain tumor image segmentation using GLISTR. MICCAI Brain Tumor Segmentation (BraTS) Challenge Manuscripts, pp. 18–19 (2014)
Kwon, D., Shinohara, R.T., Akbari, H., Davatzikos, C.: Combining generative models for multifocal glioma segmentation and registration. Med. Image Comput. Comput.-Assist. Interv. 17(1), 763–770 (2014)
Louis, D.N.: Molecular pathology of malignant gliomas. Annu. Rev. Pathol. - Mech. Dis. 1, 97–117 (2006)
Mazzara, G.P., Velthuizen, R.P., Pearlman, J.L., Greenberg, H.M., Wagner, H.: Brain tumor target volume determination for radiation treatment planning through automated MRI segmentations. Int. J. Radiat. Oncol. - Biol. - Phy. 59(1), 300–312 (2004)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.A., Arbel, T., Avants, B.B., Ayache, N., Buendia, P., Collins, D.L., Cordier, N., Corso, J.J., Criminisi, A., Das, T., Delingette, H., Demiralp, C., Durst, C.R., Dojat, M., Doyle, S., Festa, J., Forbes, F., Geremia, E., Glocker, B., Golland, P., Guo, X., Hamamci, A., Iftekharuddin, K.M., Jena, R., John, N.M., Konukoglu, E., Lashkari, D., Mariz, J.A., Meier, R., Pereira, S., Precup, D., Price, S.J., Riklin-Raviv, T., Reza, S.M.S., Ryan, M., Sarikaya, D., Schwartz, L., Shin, H.-C., Shotton, J., Silva, C.A., Sousa, N., Subbanna, N.K., Szekely, G., Taylor, T.J., Thomas, O.M., Tustison, N.J., Unal, G., Vasseur, F., Wintermark, M., Ye, D.H., Zhao, L., Zhao, B., Zikic, D., Prastawa, M., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). doi:10.1109/TMI.2014.2377694
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Nat. Acad. Sci. U.S.A. 93(4), 1591–1595 (1996)
Smith, S.M., Brady, J.M.: SUSAN - a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997)
Wen, P.Y., Kesari, S.: Malignant gliomas in adults. N. Engl. J. Med. 359(5), 492–507 (2008)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Bakas, S. et al. (2016). GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation. 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_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-30858-6_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30857-9
Online ISBN: 978-3-319-30858-6
eBook Packages: Computer ScienceComputer Science (R0)