Abstract
Brain tumor segmentation is a vital clinical requirement. In recent years, the developments of the prevalence of deep learning in medical image processing have been experienced. Automated brain tumor segmentation can reduce the diagnosis time and increase the potential of clinical intervention. In this work, we have used a patch selection methodology based on modified U-Net deep learning architecture with appropriate normalization and patch selection methods for the brain tumor segmentation task in BraTS 2020 challenge. Two-phase network training was implemented with patch selection methods. The performance of our deep learning-based brain tumor segmentation approach was done on CBICA’s Image Processing Portal. We achieved a Dice score of 0.795, 0.886, 0.827 in the testing phase, for the enhancing tumor, whole tumor, and tumor core respectively. The segmentation outcome with various radiomic features was used for Overall survival (OS) prediction. For OS prediction we achieved an accuracy of 0.570 for the testing phase. The algorithm can further be improved for tumor inter-class segmentation and OS prediction with various network implementation strategies. As the OS prediction results are based on segmentation, there is a scope of improvement in the segmentation and OS prediction thereby.
Computational support provided by Keepsake Welding Research and Skill Development Center, Center of Excellence - Welding, L. D. College of Engineering, Ahmedabad.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Leung, D., Han, X., Mikkelsen, T., Nabors, L.B.: Role of MRI in primary brain tumor evaluation. J. Natl. Compr. Canc. Netw. 12(11), 1561–1568 (2014) https://doi.org/10.6004/jnccn.2014.0156
Beets-Tan, R.G.H., Beets, G.L., Vliegen, R.F.A., Kessels, A.G.H., Van Boven, H., De Bruine, A., et al.: Accuracy of magnetic resonance imaging in prediction of tumour-free resection margin in rectal cancer surgery. Lancet 357(9255), 497–504 (2001). https://doi.org/10.1016/S0140-6736(00)04040-X
Bankman, I., (ed.) Handbook of Medical Image Processing and Analysis. Elsevier, Amsterdam (2008)
Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016). https://doi.org/10.1016/j.procs.2016.09.407
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)
Kamnitsas, K., et al.: Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 36, 61–78 (2017). [PubMed: 27865153]
Wang, G., Li, W., Ourselin, S., Vercauteren, T.: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 178–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_16
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In: International MICCAI Brainlesion Workshop, pp. 287–297. Quebec City, Quebec, Canada (2017). https://doi.org/10.1007/978-3-319-75238-9_25
Gates, E., Pauloski, J.G., Schellingerhout, D., Fuentes, D.: Glioma segmentation and a simple accurate model for overall survival prediction. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 476–484. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_42
Liang, C., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: DRINet for medical image segmentation. IEEE Trans. Med. Imaging 37(11), 2453–2462 (2018)
Varghese, A., Mohammed, S., Ganapathy, K.: Brain Tumor Segmentation from Multi Modal MR images using Fully Convolutional Neural Network. BRATS proceedings, MICCAI (2017)
Kao, P.-Y., Ngo, T., Zhang, A., Chen, J.W., Manjunath, B.S.: Brain tumor segmentation and Tractographic feature extraction from structural MR images for overall survival prediction. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 128–141. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_12
Agravat, R., Raval, M.,: Prediction of overall survival of brain tumor patients. In: TENCON 2019–2019 IEEE Region 10 Conference (TENCON), Kochi, India, pp. 31–35 (2019) https://doi.org/10.1109/TENCON.2019.8929497
Wang, F., Jiang, R., Zheng, L., Meng, C., Biswal, B.: 3D U-net based brain tumor segmentation and survival days prediction. In: Crimi, A., Bakas, S. (eds.) BrainLes 2019. LNCS, vol. 11992, pp. 131–141. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46640-4_13
https://www.med.upenn.edu/cbica/brats2020/ MICCAI BRATS - The Multimodal Brain Tumor Segmentation Challenge. https://www.med.upenn.edu/cbica/brats2020/ (2020)
Bakas, S. et al.: advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nature Sci. Data 4 170117 (2017) https://doi.org/10.1038/sdata.2017.117
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., 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., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010). https://doi.org/10.1109/TMI.2010.2046908
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022. (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Parmar, B., Parikh, M. (2021). Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-Net. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-72087-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72086-5
Online ISBN: 978-3-030-72087-2
eBook Packages: Computer ScienceComputer Science (R0)