Abstract
In this paper, we exploit a cascaded U-Net architecture to perform detection and segmentation of brain tumors (low- and high-grade gliomas) from magnetic resonance scans. First, we detect tumors in a binary-classification setting, and they later undergo multi-class segmentation. The total processing time of a single input volume amounts to around 15 s using a single GPU. The preliminary experiments over the BraTS’19 validation set revealed that our approach delivers high-quality tumor delineation and offers instant segmentation.
We applied the sequence-determines-credit approach for the sequence of authors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 1–13 (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge (2018). CoRR abs/1811.02629. http://arxiv.org/abs/1811.02629
Bauer, S., Seiler, C., Bardyn, T., Buechler, P., Reyes, M.: Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration. In: Proceedings of the IEEE EMBC, pp. 4080–4083 (2010). https://doi.org/10.1109/IEMBS.2010.5627302
Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Exp. Syst. Appl. 38(5), 4998–5004 (2011)
Fan, X., Yang, J., Zheng, Y., Cheng, L., Zhu, Y.: A novel unsupervised segmentation method for MR brain images based on fuzzy methods. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 160–169. Springer, Heidelberg (2005). https://doi.org/10.1007/11569541_17
Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2), 378–390 (2011)
Ghafoorian, M., et al.: Location sensitive deep convolutional neural networks for segmentation of white matter hyperintensities (2016). CoRR abs/1610.04834
Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Ji, S., Wei, B., Yu, Z., Yang, G., Yin, Y.: A new multistage medical segmentation method based on superpixel and fuzzy clustering. Comp. Math. Meth. Med. 2014, 747549:1–747549:13 (2014)
Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38
Korfiatis, P., Kline, T.L., Erickson, B.J.: Automated segmentation of hyperintense regions in FLAIR MRI using deep learning. Tomography J. Imaging Res. 2(4), 334–340 (2016). https://doi.org/10.18383/j.tom.2016.00166
Ladgham, A., Torkhani, G., Sakly, A., Mtibaa, A.: Modified support vector machines for MR brain images recognition. In: Proceedings of CoDIT, pp. 032–035 (2013). https://doi.org/10.1109/CoDIT.2013.6689515
Marcinkiewicz, M., Nalepa, J., Lorenzo, P.R., Dudzik, W., Mrukwa, G.: Segmenting brain tumors from MRI Using cascaded multi-modal U-Nets. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 13–24. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_2
Mei, P.A., de Carvalho Carneiro, C., Fraser, S.J., Min, L.L., Reis, F.: Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps. J. Neurol. Sci. 359(1–2), 78–83 (2015)
Menze, et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imag. 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., Isgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans, Med. Imaging 35(5), 1252–1261 (2016). https://doi.org/10.1109/TMI.2016.2548501
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Nalepa, J., Kawulok, M.: Adaptive genetic algorithm to select training data for support vector machines. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 514–525. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45523-4_42
Nalepa, J., Kawulok, M.: Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs. Neurocomputing 185, 113–132 (2016)
Park, M.T.M., et al.: Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates. NeuroImage 95, 217–231 (2014)
Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D.M.L.D., Silva, C.A.: Brain tumour segmentation based on extremely rand. forest with high-level features. In: Proceedings of IEEE EMBC, pp. 3037–3040 (2015). https://doi.org/10.1109/EMBC.2015.7319032
Pipitone, J., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. NeuroImage 101, 494–512 (2014)
Rajendran, A., Dhanasekaran, R.: Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: a combined approach. Procedia Eng. 30, 327–333 (2012). https://doi.org/10.1016/j.proeng.2012.01.868
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015). CoRR abs/1505.04597
Saha, S., Bandyopadhyay, S.: MRI brain image segmentation by fuzzy symmetry based genetic clustering technique. In: Proceedings of IEEE CEC, pp. 4417–4424 (2007). https://doi.org/10.1109/CEC.2007.4425049
Sauwen, N., et al.: Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin. 12, 753–764 (2016)
Sauwen, N., Acou, M., Sima, D.M., Veraart, J., Maes, F., Himmelreich, U., Achten, E., Huffel, S.V.: Semi-automated brain tumor segmentation on multi-parametric mri using regularized non-negative matrix factorization. BMC Med. Imaging 17(1), 29 (2017)
Simi, V., Joseph, J.: Segmentation of glioblastoma multiforme from MR images - a comprehensive review. Egypt. J. Radiol. Nucl. Med. 46(4), 1105–1110 (2015)
Soltaninejad, M., et al.: Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. Int. J. Comp. Assist. Radiol. Surg. 12(2), 183–203 (2017)
Taherdangkoo, M., Bagheri, M.H., Yazdi, M., Andriole, K.P.: An effective method for segmentation of MR brain images using the ant colony optimization algorithm. J. Dig. Imaging 26(6), 1116–1123 (2013)
Verma, N., Cowperthwaite, M.C., Markey, M.K.: Superpixels in brain MR image analysis. In: Proceedings of IEEE EMBC, pp. 1077–1080 (2013). https://doi.org/10.1109/EMBC.2013.6609691
Villanueva-Meyer, J.E., Mabray, M.C., Cha, S.: Current clinical brain tumor imaging. Neurosurgery 81(3), 397–415 (2017). https://doi.org/10.1093/neuros/nyx103
Wu, W., Chen, A.Y.C., Zhao, L., Corso, J.J.: Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int. J. Comput. Assist. Radiol. Surg. 9(2), 241–253 (2013). https://doi.org/10.1007/s11548-013-0922-7
Zhao, J., Meng, Z., Wei, L., Sun, C., Zou, Q., Su, R.: Supervised brain tumor segmentation based on gradient and context-sensitive features. Front. Neurosci. 13, 144 (2019). https://doi.org/10.3389/fnins.2019.00144, https://www.frontiersin.org/article/10.3389/fnins.2019.00144
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., Fan, Y.: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation (2017). CoRR abs/1702.04528
Zhuge, Y., Krauze, A.V., Ning, H., Cheng, J.Y., Arora, B.C., Camphausen, K., Miller, R.W.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys., 1–10 (2017). https://doi.org/10.1002/mp.12481
Zikic, D., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_46
Acknowledgments
This research was supported by the National Centre for Research and Development (POIR.01.02.00-00-0030/15). JN was supported by the Silesian University of Technology funds (02/020/BKM19/0183).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kotowski, K., Nalepa, J., Dudzik, W. (2020). Detection and Segmentation of Brain Tumors from MRI Using U-Nets. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11993. Springer, Cham. https://doi.org/10.1007/978-3-030-46643-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-46643-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46642-8
Online ISBN: 978-3-030-46643-5
eBook Packages: Computer ScienceComputer Science (R0)